Trends in Asthma Prevalence and Incidence in Ontario, Canada, 1996-2005: A Population Study
Bibliographic record
Abstract
In the 1980s and early 1990s, asthma prevalence increased significantly in most Westernized countries. In more recent years, asthma trends have been less clear, with some studies suggesting that they are still rising and others suggesting that they have stabilized or decreased. A population-based cohort study was conducted to estimate asthma prevalence and incidence trends in one large Canadian province, Ontario. All individuals with asthma living in Ontario, a province of Canada with a multicultural population of approximately 12 million, were identified in universal, population health administrative databases by using a validated health administrative case definition of asthma. Annual asthma prevalence, incidence, and all-cause mortality rates were estimated from 1996 to 2005. During this time, the prevalence of asthma increased by 70.5%. The age- and sex-standardized asthma prevalence increased from 8.5% in 1996 to 13.3% in 2005, a relative increase of 55.1% (P < 0.0001). Asthma incidence rates increased in children by 30.0% and were relatively stable in adults. Overall all-cause mortality decreased. Asthma prevalence in Ontario, Canada, has increased significantly. This is attributable, in part, to an increase in the incidence of asthma in children. Effective clinical and public health strategies are needed to prevent and manage asthma in the population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".