Trends in pediatric ambulatory community acquired infections before and during COVID-19 pandemic: A prospective multicentric surveillance study in France
Notice bibliographique
Résumé
Background: The concept of immunity debt caused by non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic raised several concerns. We aimed to support this concept by analyzing the trends of community-acquired pediatric infectious diseases before and up to two years after the onset of the pandemic. Methods: We conducted a prospective surveillance study in France with 107 pediatricians. From January 2018 to April 2022, the electronic medical records of children with an infectious disease were automatically extracted. The annual number of infectious diseases in 2020 and 2021 was compared to 2018-2019 and their frequency was compared by logistic regression.Findings: From 2018 to 2021, 185,368 infectious diseases were recorded. Compared to 2018-2019, the number decreased in 2020 by about a third (from 98,783 to 35,432). Frequency of scarlet fever, tonsillopharyngitis, enteroviral infections, bronchiolitis, and gastroenteritis decreased with OR varying from 0·6 to 0·9, p<0·001. In 2021, among the 52,153 infectious diagnoses, an off-season rebound was observed with increased frequency of enteroviral infections, bronchiolitis, gastroenteritis and otitis with OR varying from 1·1 to 1·5, p<0·001. Children with chickenpox in 2021 were older by 1·9 months than in 2018-2019 (p<0·001).Interpretation: The unusual uptick of several infectious diseases after the partial relaxation of NPIs, at times with changed seasonality and age, underlines the interest of immunity debt, which could be attenuated through high vaccine coverage and an updated nationwide immunization schedule. These findings also highlight the need for continuous surveillance of infectious diseases, especially insofar as future epidemics are largely unpredictable.Trial Registration Details: The study was registered at ClinicalTrials.gov (NCT04471493).Funding Information: The study was supported by Association Clinique et Thérapeutique Infantile du Val de Marne (ACTIV), French Pediatrician Ambulatory Association (AFPA) and unrestricted grants of GSK, MSD, Pfizer and Sanofi.Declaration of Interests: RC reports grants to the institution ACTIV, personal fees, and nonfinancial support from GSK, Sanofi, Pfizer, and Merck, outside the submitted work. AR reports travel grants from Pfizer and AstraZeneca. FA reports receiving personal fees from MSD, AstraZeneca, Sanofi and Pfizer.NO reports travel grants from Pfizer, Sanofi, and GSK, outside the submitted work. BF is employed by CompuGroup Medical. CL reports grants to the institution ACTIV from GSK, Sanofi, Pfizer, and Merck, and personal fees and nonfinancial support from Pfizer and Merck, outside the conduct of the study. All other authors report no potential conflicts.Ethics Approval Statement: Parents and children were informed of the study before electronic data capture began and data were collected only in the event of non-refusal. The study was approved by the French National Commission on Informatics and Liberty (no. 1921226) and by an ethics committee (CHI Créteil Hospital, France).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».