0476 THE EPIDEMIOLOGY OF OBSTRUCTIVE SLEEP APNEA AND ASTHMA OR CHRONIC OBSTRUCTIVE PULMONARY DISEASE OVERLAP SYNDROMES IN ONTARIO, CANADA: A POPULATION-BASED COHORT STUDY
Bibliographic record
Abstract
Despite their importance, the epidemiology of obstructive sleep apnea (OSA) and asthma (OSA/asthma), and OSA and chronic obstructive pulmonary disease (OSA/COPD) overlap syndromes have not been well studied. We conducted a population-based cohort study to estimate trends in the prevalence and mortality of individuals 35 years and older with OSA/asthma or OSA/COPD in Ontario, Canada. Ontario has a diverse population of about 13 million and a universal health care system that covers most medical services including polysomnography and positive airway pressure (PAP) treatment. Validated health administrative case definitions were used to identify individuals with moderate to severe asthma or COPD. Individuals with OSA were those who received OSA-related PAP treatment. Age- and sex-standardized annual prevalence and mortality rates were estimated and compared from 2009 to 2013 and between conditions. The highest prevalence for both overlaps was in those 50 to 79-years and men. The standardized prevalence increased from 2009 to 2013: for OSA/asthma from 0.38% to 0.54% of the population, for OSA/COPD from 0.23% to 0.31%. Both overlap syndromes were associated with higher mortality than OSA alone. Higher mortality was associated with OSA/asthma compared to asthma alone (OR = 1.09, 95%CI: 1.05–1.13) with larger differences in women and those younger than 50 years. In women but not men, a higher mortality was associated with OSA/COPD compared to COPD alone: OR = 1.06 (1.01–1.11). The standardized all-cause mortality rates among individuals with both overlap syndromes decreased modestly over time. In a large North American population, the prevalence of overlap syndromes was higher in men and middle-aged individuals, and increasing over time. Overlap syndromes were associated with excess mortality in spite of funded PAP treatment, particularly in women and younger people. These findings can alert health care providers and policy makers to the large and increasing burden of overlap syndromes and which high-risk groups are most affected. The Godfrey S Pettit Block Term Grants, University of Toronto; Canadian Respiratory Research Network fellowship training award; Institute for Clinical Evaluative Sciences, which is funded by an annual grant from the Ontario Ministry of Health and Long-term Care.
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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 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".