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

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

Bibliographic record

VenueEpidemiology · 2003
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsAsthmaMedicineDemographyDescriptive statisticsHospital admissionConfoundingPediatricsEmergency departmentStatisticsPsychiatry

Abstract

fetched live from 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.

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)0.015
metaresearch head score (Gemma)0.131
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.131
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.128
GPT teacher head0.418
Teacher spread0.289 · 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.

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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