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Record W4403684625 · doi:10.1093/pch/pxae067.041

42 Clinical features and severity of COVID-19 with respiratory virus coinfections versus SARS-CoV-2 monoinfection in hospitalized children: A Canadian national surveillance study

2024· article· en· W4403684625 on OpenAlexaboutno aff
Costanza Di Chiara, Daniel S. Farrar, Julie A. Bettinger, Aaron Campigotto, Shelley L. Deeks, Olivier Drouin, Joanne Embreé, Scott A. Halperin, Taj Jadavji, Kescha Kazmi, Charlotte Moore Hepburn, Jesse Papenburg, Rupeena Purewal, Manish Sadarangani, Laura Sauvé, Karina A. Top, Fatima Kakkar, Shaun K. Morris

Bibliographic record

VenuePaediatrics & Child Health · 2024
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Medicine2019-20 coronavirus outbreakRespiratory systemBetacoronavirusSars virusVirologyVirusInternal medicineOutbreakDisease

Abstract

fetched live from OpenAlex

Abstract Background The co-circulation of SARS-CoV-2 alongside other respiratory viruses has led to the risk of coinfections, potentially intensifying the severity of cases, especially in children. Objectives This study examined the epidemiology, clinical characteristics, and outcomes of SARS-CoV-2 respiratory virus coinfections in comparison to SARS-CoV-2 monoinfections in hospitalized children. Design/Methods A nationwide surveillance study was conducted to assess paediatric COVID-19-related hospitalizations across Canada from April 2020 to May 2022. Data were captured through two datasets covering ~90% of all Canadian tertiary-care paediatric beds: The Canadian Paediatric Surveillance Program and the Canadian Immunization Monitoring Program, ACTive. Coinfections were defined as the simultaneous detection of SARS-CoV-2 and ≥1 other respiratory virus. Severe infection was defined as intensive care, ventilatory, or hemodynamic support needs, organ systems complications, or death. Variables and outcomes were summarized and compared, and risk ratios were computed using Poisson regression, adjusting for age, gender, comorbid conditions, SARS-CoV-2 lineage, and vaccination status. Results Out of 1501 COVID-19-related hospitalizations, 163 (10.9%) had documented coinfections with 44/163 [27%] involving SARS-COV-2 plus ≥2 other respiratory viruses. The majority of SARS-CoV-2 monoinfections (1176/1501, 87.9%) and coinfections (139/163, 85.3%) belonged to the Omicron era (Figure A). RSV (66/163, 40.5%) and Enterovirus/rhinovirus (61/163, 37.4%) coinfections were the most common. Coinfection cases were significantly younger than monoinfection cases (median age 1.2 [IQR:0.3-3.3] vs 1.6 [IQR:0.3-7.0] years, p=0.04). Overall, severe disease was more common among cases with any coinfection (38.0%) than SARS-CoV-2 monoinfection (25.7%; adjusted risk ratio 1.45, 95% confidence interval 1.17-1.80) (Figure B). In particular, we observed that severe outcomes were significantly higher in SARS-CoV-2 coinfections compared to monoinfections during the Omicron era (Figure C-D). Overall, 26/61 (42.6%) Enterovirus/Rhinovirus and 20/66 (30.3%) RSV coinfections were associated with severe outcomes. Conclusion Children with documented coinfections had more severe respiratory disease compared to SARS-COV-2 monoinfections. However, children with severe COVID-19 may have been more likely tested for multiple viruses, leading to a risk of underestimation of coinfections among children with milder disease. Further work is needed to assess how different virus coinfections may affect children and which, if any, may be a predominant driver of disease severity. 1Severe disease was defined as intensive care, ventilatory, or hemodynamic support requirements, organ system complications, or death. 2Lineage was first assigned based on available genetic sequencing data. If missing, lineage was imputed based on the dominant circulating lineage at the provincial level according to the GISAID database.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.206
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.049
GPT teacher head0.404
Teacher spread0.355 · 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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Citations0
Published2024
Admission routes1
Has abstractyes

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