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Record W4412838446 · doi:10.2196/73047

Comparative Analysis of Outcomes of Influenza and COVID-19 Admissions Among Children With Asthma: A Nationwide Retrospective Cohort Study Using the US National Readmissions Database

2025· article· en· W4412838446 on OpenAlexvenueno aff
Chia-Pi Cheng, Jinn-Li Wang, Ying-Chun Lu

Bibliographic record

VenueJMIR Medical Informatics · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintAsthmaMedicineCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakDatabasePediatricsVirologyWorld Wide WebComputer scienceImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Asthma is a common chronic respiratory disease with increasing prevalence among children over the past few decades. It can cause significant respiratory symptoms and acute exacerbations, often requiring emergency care or hospitalization. Moreover, exposure to respiratory viral infections, such as COVID-19 and influenza, can trigger severe complications in children with asthma. Despite these concerns, few studies have directly compared the in-hospital outcomes of children with asthma experiencing these infections. OBJECTIVE: This study aimed to compare the in-hospital outcomes of these infections in children with asthma from a population-based perspective. METHODS: We conducted a population-based retrospective cohort study using data from the 2020 US Nationwide Readmissions Database. Children aged 1 to 19 years with asthma who were admitted for COVID-19 or influenza were eligible for inclusion. Outcomes evaluated included in-hospital mortality, major complications, and 90-day readmission rate. Survey-weighted logistic regression models were used to compare clinical outcomes between the two infection groups, adjusting for demographic and clinical characteristics. RESULTS: A total of 1472 hospitalized children with asthma were included, of whom 405 (27.5%) were admitted for COVID-19 and 1067 (72.5%) for influenza. After adjustment, the multivariate analysis revealed that children admitted for COVID-19 had a significantly higher risk of sepsis or shock (adjusted odds ratio [aOR] 4.30, 95% CI 1.79-10.32) but a lower risk of bacterial or fungal pneumonia (aOR 0.37, 95% CI 0.23-0.61) compared with those admitted for influenza. Stratified analyses by age revealed that among children aged 1 to 5 years, the risk of 90-day readmission was significantly higher for those with COVID-19 than for those with influenza (aOR 3.02, 95% CI 1.09-8.35). No significant difference in in-hospital mortality was detected between the two infection groups in either the multivariable model or any of the age-stratified analyses. CONCLUSIONS: US children with asthma hospitalized for COVID-19 had higher risks of sepsis or shock compared to those admitted for influenza. In contrast, children admitted for influenza had a higher risk for bacterial or fungal pneumonia. After stratifying by age, children aged 1 to 5 years with COVID-19 had a significantly higher risk of 90-day readmission than those with influenza. Our findings suggest that different clinical approaches may be needed for children with asthma, depending on infection etiology and patient age.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.466
Teacher spread0.395 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

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