Patient-reported Differences in Obstructive Sleep Apnea Care between Jurisdictions with and without Government Funding for Continuous Positive Airway Pressure
Bibliographic record
Abstract
Abstract Rationale Funding for obstructive sleep apnea (OSA) treatment may impact how patients access care, wait times, and costs of care. Objectives The aim of this study was to compare differences in diagnosis and treatment of OSA between Canadian jurisdictions with and without public funding for continuous positive airway pressure (CPAP). Methods We administered an anonymous internet survey to Canadian adults reporting a physician diagnosis of OSA. Responses were categorized on the basis of whether the respondent’s province provided full or partial funding for CPAP therapy for all patients. We assessed wait times for diagnosis and treatment, patient-borne costs, and model of care delivery compared between jurisdictions with and without universal CPAP funding. Results We received 600 responses representing all Canadian provinces and territories. The median (interquartile range) age was 59 (49–66) years; 57% were male, and 21% were from rural settings. Patients living in provinces without public CPAP funding (n = 419) were more likely to be diagnosed using home sleep apnea testing (69% vs. 20%; P = 0.00019). Wait times were similar after adjustment for demographics, disease characteristics, and model of care. Although patient-borne costs of care were similar between jurisdictions, patients from regions without CPAP funding reported that cost had a greater influence on the choice of therapy. Sleep specialists were more commonly involved in OSA care in regions with CPAP funding. There was no difference in the current use of therapy between jurisdictions with and without public funding. Conclusions This survey study demonstrates that public funding for CPAP therapy impacts how Canadians access OSA care but is not associated with differences in wait times or costs. Future research is required to determine the impact of different funding models for OSA care on clinical 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.003 | 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".