Is asthma a vanishing disease? A study to forecast the burden of asthma in 2022
Bibliographic record
Abstract
BACKGROUND: Recent evidence regarding temporal trends of asthma burden has not been consistent, with some countries reporting decreases in prevalence of asthma. In Ontario, the province in Canada with the highest population, the prevalence of asthma rose at a rate of 0.5% per year between 1996 and 2005. These estimates were based on population-based health services use data spanning more than a decade and provide a powerful source to forecast the trends of asthma burden. The objective of this study was to use observed population trends data of asthma incidence and prevalence to forecast future disease burden. METHODS: The Ontario Asthma Surveillance Information System (OASIS) used health administrative databases to identify and track all individuals in the province with asthma. Individuals with asthma identified between April 1, 1996 and March 31, 2010 were included. Exponential smoothing models were applied to annual data to project incidence to the year 2022, prevalence was estimated by applying the cumulative projected incidence to the projected population. RESULTS: While asthma incidence is falling, the absolute number of prevalent cases will continue to rise. We projected that almost 1 in 8 individuals in Ontario will have asthma by the year 2022, suggesting that asthma will continue to be a major burden on individuals and the health care system. CONCLUSIONS: These projections will help inform health care planners and decision-makers regarding resource allocation to optimize asthma outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".