Temporal and Regional Trends in Obstructive Sleep Apnea Using Administrative Health Data in Alberta, Canada
Bibliographic record
Abstract
Abstract Rationale Obstructive sleep apnea (OSA) is a common treatable condition with important health and societal consequences. Objectives We aimed to assess the annual incidence and prevalence of clinically recognized and geographic clustering of OSA in Alberta, Canada, using administrative health data case definitions. Methods We used two administrative health databases in Alberta to identify ICD-9 and ICD-10 (International Classification of Diseases, Ninth and 10th Revisions, respectively) diagnostic codes for adults and children at least 2 years old diagnosed with OSA between 2003 and 2020. We defined OSA using an algorithm developed and validated in Alberta: at least three claims or one hospitalization within 2 years. We mapped residential postal codes to 70 subregional health authorities (SRHAs). Crude, age group- and sex-specific incidence and prevalence, and age group- and sex-standardized rates were calculated for Alberta and SRHAs. Spatial scan statistics identified clusters of SRHAs in which OSA cases were higher (hot spots) or lower (cold spots) than expected. Results Between 2003 and 2020, OSA prevalence increased from 0.14% to 4.59%. The annual incidence of OSA increased after 2013. Incidence and prevalence were higher in older adults and children aged 2–11 years compared with 12–17 years. Cluster analysis revealed regional variation in OSA incidence and prevalence over time with no consistent pattern except for cold spots in one large metropolitan center (Calgary). Conclusions From 2003 to 2020, the incidence and prevalence of clinically recognized OSA increased but varied by geography. Administrative health data can be used to guide interventions aimed at improving health service delivery and the quality of OSA 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".