Coexistence of clinically significant obstructive sleep apnea with physician-diagnosed asthma or chronic obstructive pulmonary disease: A population study of prevalence and mortality
Bibliographic record
Abstract
RATIONALE Despite potential importance, the epidemiology of coexisting obstructive sleep apnea and asthma (OSA/asthma) or OSA and COPD (OSA/COPD) has not been well studied.OBJECTIVES To access the trends in prevalence and mortality of coexisting OSA/asthma or OSA/COPD among individuals 35-years or older in Ontario, Canada.METHODS AND MEASUREMENTS We conducted a population-based study using provincial health administrative data. Validated case definitions were used to identify individuals with physician-diagnosed asthma or COPD. Individuals with clinically significant OSA were those who initiated positive airway pressure treatment. Age- and sex-standardized annual prevalence and mortality rates were estimated and compared from 2009 to 2015. Generalized linear models were used.MAIN RESULTS The standardized prevalence increased from 0.42% to 0.66% for OSA/asthma and from 0.23% to 0.35% for OSA/COPD from 2009 to 2015. The standardized all-cause mortality rates remained stable over time. Both coexistence conditions were associated with higher mortality than OSA alone. Adjusting for age, sex and calendar year, mortality was modestly but statistically significantly higher for OSA/asthma than asthma alone (OR = 1.03; 1.00–1.06) with the highest OR noted in 35-49 years old group (1.85; 1.63–2.09). There was no statistical difference in all-cause mortality among individuals with coexisting OSA/COPD compared to patients with COPD alone. In women only, OSA/COPD was associated with a modestly higher mortality than COPD alone (OR = 1.05; 1.01–1.09).CONCLUSIONS In this population-based study of coexisting clinically significant OSA and chronic lung disease, we report population prevalence and mortality, including their age and sex distribution. These findings can alert health care providers and policymakers to the large and increasing burden of these coexisting conditions and high-risk groups.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".