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Enregistrement W2076302615 · doi:10.1097/00001648-200309001-00272

THE ASSOCIATION BETWEEN HOSPITAL ADMISSIONS FOR CHILDHOOD ASTHMA AND RETURN TO SCHOOL IN SYDNEY, AUSTRALIA, 1994 TO 2000

2003· article· en· W2076302615 sur OpenAlexaboutno aff
Geoffrey Morgan, D Lincoln, Vicky Sheppeard, B Jalaludn, James Beard, Stephen W. Corbett

Notice bibliographique

RevueEpidemiology · 2003
Typearticle
Langueen
DomaineDecision Sciences
Thématiquedemographic modeling and climate adaptation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAsthmaMedicineDemographyDescriptive statisticsHospital admissionConfoundingPediatricsEmergency departmentStatisticsPsychiatry

Résumé

récupéré en direct d'OpenAlex

Introduction Multiple peaks in hospital admission rates for children with severe acute asthma have been observed throughout the year in several countries including Australia, Canada, the United States, the United Kingdom and New Zealand. The largest peaks consistently occur in the weeks following the end of the long summer holiday. It has been hypothesized that children are more likely to be exposed to viral respiratory infections on return to school, exacerbating asthma and leading to higher admission rates. We examine the seasonal pattern of hospital admissions of children for severe acute asthma in Sydney between 1994 and 2000 and investigate the association between these asthma episodes and return to school. Methods We aggregated hospital admission data for the Sydney metropolitan area to obtain daily counts of child asthma (1–14 years) admissions from 1994 to 2000. Only admissions referred from emergency departments were included. We assessed a range of descriptive statistics and time series plots to describe long term and seasonal trends, including unusual episodes. Negative binomial regression was used for a cross-sectional analysis to assess the effect of school terms relative to school holidays on the risk of hospital admission. Time series analysis using generalized additive models investigated the effect of returning to school on hospital admissions while adjusting for a range of potential confounding effects including: long term and seasonal trends, weather, fluepidemics, and day of the week. We also conducted sensitivity analyses on the influence of various time series modeling approaches. Results Descriptive plots and statistics for various time periods clearly indicate differences in the admission rates during school terms and school holidays. Over the study period, the median number of admissions was 13 per day during school terms and 8 per day during school holidays (z = −14.15, p < 0.0001) and this contrast was reflected in each year. The cross-sectional analysis indicated that a child's risk of admission to hospital for asthma is 60% greater during school term than school holidays (RR = 1.59, p < 0.0001, 95% CI = (1.52, 1.68)). Time series analyses indicate that, after controlling for potential confounders, the risk of admissions for childhood asthma increase steeply to a maximum 3–4 weeks after the start of school in terms 1, 2 and 4 and then steadily decreases. There is little change in asthma risk throughout term. The maximum risk of asthma admissions in term 1 is more than double that of days outside term1. The maximum risk of asthma admissions in term 2 and term 4, compared to days outside these periods, is about half the term 1 maximum. Conclusions Our analysis indicates a substantial increase in the risk of asthma associated with return to school, with the effect peaking about 3 weeks after the long summer holiday. This is consistent with previous studies in both the northern and southern hemisphere and provides supporting evidence for the hypothesis that this peak is associated with increased childhood exposure to viral infection. Preventive measures focused on return to school, especially after the long summer holiday, have the potential to substantially decrease childhood asthma admissions.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,015
score de la tête « metaresearch » (Gemma)0,131
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,140
Score d'incertitude au seuil0,876

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0150,131
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,128
Tête enseignante GPT0,418
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2003
Routes d'admission1
Résumé présentoui

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