Projecting the future burden of OSA in EU5 countries accounting for body mass index
Bibliographic record
Abstract
Rationale: Obstructive sleep apnea (OSA) is a sleep disorder associated with intermittent hypoxia, sleep fragmentation, and daytime impairment. In Europe, aging populations and rising obesity rates contribute to the increasing burden of OSA. With projected increases in BMI and an aging demographic structure, OSA prevalence is expected to rise. We aimed to project the future burden of OSA in the EU-5 (France, Germany, Italy, Spain, and the UK) through 2050, accounting for regional demographic shifts and BMI trends. Methods: An open-cohort Markov model was developed to simulate population dynamics from 2019 to 2050 across age, sex, and BMI categories. OSA prevalence was calibrated within these subgroups, and severity distributions were modeled based on the apnea-hypopnea index (AHI). Results: Among adults aged 30–69 in the EU-5, OSA prevalence (AHI ≥ 5) is projected to increase from 33.1% (51.3 million (M) cases) in 2020 to 52.3% (76.6M cases) by 2050. Country-specific relative increases include France (+68.3%), Germany (+28.5%), Italy (+64.9%), Spain (+31.0%), and the United Kingdom (+66.1%). By 2050, OSA prevalence is estimated to reach 55.7% in males and 48.9% in females (Table). Conclusion: We projected a significant rise in OSA prevalence across the EU-5 due to increasing BMI and an aging population. These findings underscore the need for proactive public health strategies to identify and manage the growing burden of OSA. erj;66/suppl_69/OA4354/F1 F1 F1
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".