Air pollution, weather and positive airway pressure treatment adherence in adults with sleep apnea: a retrospective community‐based repeated‐measures longitudinal study
Bibliographic record
Abstract
Summary We assessed the relation between air pollution, weather, and adherence to positive airway pressure (PAP) therapy in a retrospective community‐based repeated‐measures study of adults with obstructive sleep apnea who purchased PAP devices from a registered provider between 2013 and 2017 (Ottawa, Ontario, Canada) and had at least one day of data. Daily PAP‐derived data, air pollution, and weather databases were linked using postal code. The exposures were mean nocturnal (8:00 p.m. to 8:00 a.m.) (i) residential concentrations of nitrogen dioxide (NO 2 ), fine particulate matter <=2.5 μm (PM 2.5 ), ozone (O 3 ), and Air Quality Health Index (AQHI), and (ii) temperature, relative humidity, and barometric pressure. Covariates in the main model were demographics, season, exposure year, and PAP therapy mode. We analysed 8148 adults (median age of 54 years and 61% men) and 2,071,588 days of data. Based on daily data, the median (interquartile range) daily PAP usage was 416 (323–487) min. Using mixed‐effect regression analyses to incorporate daily data and clustering by individuals, we found a statistically significant decrease in adherence for increased levels of NO 2 , PM 2.5 , and AQHI. The largest effect was for NO 2 : a decrease in daily PAP use while comparing the highest versus lowest quartiles (Qs) was 3.4 (95% confidence interval [CI] 2.8–3.9) min. Decreased PAP adherence was also associated with increased temperature (Q4 versus Q1: 2.6 [95% CI: 1.5–3.7] min) and decreased barometric pressure (Q1 versus Q4: 2.0 [95% CI 1.5–2.5] min). We observed modest but statistically significant acute effects of air pollution and weather on daily PAP adherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".