Longer-term impacts of the COVID-19 pandemic on obstructive sleep apnoea (OSA)-related healthcare: a province-based study
Bibliographic record
Abstract
RATIONALE: Following marked reductions in sleep medicine care early in the COVID-19 pandemic, there is limited information about the recovery of these services. We explored long-term trends in obstructive sleep apnoea (OSA) health services and service backlogs during the pandemic compared with pre-pandemic levels in Ontario (the most populous province of Canada). METHODS: In this retrospective population-based study using Ontario (Canada) health administrative data on adults, we compared rates of polysomnograms (PSGs), outpatient visits and positive airway pressure (PAP) therapy purchase claims during the pandemic (March 2020 to December 2022) to pre-pandemic rates (2015-2019). We calculated projected rates using monthly seasonal time series auto-regressive integrated moving-average models based on similar periods in previous years. Service backlogs were estimated from the difference between projected and observed rates. RESULTS: Compared with historical data, all service rates decreased at first during March to May 2020 and subsequently increased. By December 2022, observed service rates per 100 000 persons remained lower than projected for PSGs (September to December 2022: 113 vs 141, 95% CI: 121 to 163) and PAP claims (September to December 2022: 50 vs 60, 95% CI: 51 to 70), and returned to projected for outpatient OSA visits. By December 2022, the service backlog was 193 078 PSGs (95% CI: 139 294 to 253 075) and 57 321 PAP claims (95% CI: 27 703 to 86 938). CONCLUSION: As of December 2022, there was a sustained reduction in OSA-related health services in Ontario, Canada. The resulting service backlog has likely worsened existing problems with underdiagnosis and undertreatment of OSA and supports the adoption of flexible care delivery models for OSA that include portable technologies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".