A population based time series analysis of asthma hospitalisations in Ontario, Canada: 1988 to 2000
Bibliographic record
Abstract
Background: Asthma is a common yet incompletely understood health problem associated with a high morbidity burden.A wide variety of seasonally variable environmental stimuli such as viruses and air pollution are believed to influence asthma morbidity.This study set out to examine the seasonal patterns of asthma hospitalisations in relation to age and gender for the province of Ontario over a period of 12 years.Methods: A retrospective, population-based study design was used to assess temporal patterns in hospitalisations for asthma from April 1, 1988 to March 31, 2000.Approximately 14 million residents of Ontario eligible for universal healthcare coverage during this time were included for analysis.Time series analyses were conducted on monthly aggregations of hospitalisations.Results: There is strong evidence of an autumn peak and summer trough seasonal pattern occurring every year over the 12-year period (Fisher-Kappa (FK) = 23.93,p > 0.01; Bartlett Kolmogorov Smirnov (BKS) = 0.459, p < 0.01).This pattern was observed in both sexes.However, young males (0-4 years) were hospitalised at two to three times the rate of females of the same age.Rates were much lower in the older age groups.A downward trend in asthma hospitalisations was observed in the total population over the twelve-year period (beta = -0.980,p < 0.01).Conclusions: A clear and consistent seasonal pattern was observed in this study for asthma hospitalisations.These findings have important implications for the development of effective management and prevention strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".