Impacts of the SARS-CoV-2 pandemic on the seasonal pattern of hospitalizations for acute respiratory diseases among children in Alberta, Canada
Notice bibliographique
Résumé
Introduction: Acute infectious respiratory diseases (ARD) among children generally have a biennial pattern – peak incidence is highest every other winter. This seasonal pattern of ARD was interrupted in 2020 by SARS-CoV-2 and non-pharmaceutical interventions (NPI). We conducted a population based retrospective cohort study in Alberta, that measured the impact on (i) the weekly incidence of hospitalizations to quantify healthcare use, (ii) the weekly percent of PICU admissions to monitor clinical severity, and (iii) the weekly average age at discharge to characterise the affected population. Methods: From Apr 2003-Dec 2023, all hospital discharges and PICU admissions for ARD (i.e. bronchiolitis, pneumonia, influenza-like-illness, and croup) among children < 18 years old were identified in the provincial hospital Discharge Abstract Database. Weekly incidence of hospital discharge was calculated using population denominators. Weekly percent PICU admissions was calculated using all hospital discharges as the denominator. Weekly average age at discharge was calculated from birth to discharge in months. Seasonal autoregressive-integrated-moving-average (SARIMA) models predicted the expected weekly outcomes from Apr 2020 onward. Incidence ratios and percent change compared observed versus expected outcomes. Analyses were conducted in R version 4.2.2 (2022-10-31) and R studio build 2022.12.0+353. Results: There were 63,776 hospitalizations for ARD among children from Apr 2003-Dec 2023: 22,963 (36.01%) for bronchiolitis, 23,977 (37.44%) for pneumonia, 10,833 (16.97%) for influenza-like-illness, and 4,984 (7.81%) for croup. Of the hospitalizations, 4,167 (6.53%) included a PICU admission. The average weekly incidence of hospitalization for ARD per 100,000 children decreased 12.71-fold during Dec 2020-Feb 2021 (0.82 observed vs. 10.42 [95%CI 5.11, 15.73] expected) and increased 1.51-fold during Dec 2022-Feb 2023 (16.28 observed vs. 10.77 [95%CI 4.71, 16.83] expected). The average percentage of PICU admissions steadily increased from 4.07% (95%CI 1.22%, 6.91%) in Dec 2003-Feb 2004 to 10.48% (95%CI 8.36%, 12.60%) in Dec 2019-Feb 2020. There was no significant change in the percentage of PICU admissions in Dec 2020-Feb 2021 and Dec 2022-Feb 2023, 11.17% (95%CI 0.00%, 26.32%) and 11.86% (95%CI 9.33%, 14.39%) respectively. During each winter season, the average age at discharge decreased to 25 months (95%CI 17.85, 33.74) annually. Similar patterns for incidence of hospitalizations, percent PICU admissions, and average age at discharge were observed for bronchiolitis, pneumonia, influenza-like-illness, and croup. Discussion: SARS-CoV-2 and NPI had significant impacts on provincial hospitalization for ARD among children. Initially hospitalizations for ARD decreased 12.71-fold during Dec 2020-Feb 2021. With SARS-CoV-2 vaccine availability, increased population immunity, and relaxation of NPI, hospitalizations for ARD increased 1.51-fold during Dec 2022-Feb 2023. However, there was no change in clinical severity based on percent PICU admissions, and no change in affected population based on average age at discharge.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».