0477 Americas Prevalence of OSA in Adults: Estimation Using Currently Available Data
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a major clinical problem due to its high prevalence and devastating complications. At present, the prevalence of adult OSA in the Americas (including North and South America) is unclear. Limited epidemiological studies have been done and the majority of the Americas’ population has not had a reliable estimate of disease burden. We sought to estimate the Americas’ prevalence of adult OSA using existing data from epidemiological studies. We contacted authors of the important analyses following an exhaustive review of the literature. For countries where no measurement had been made, we used publicly available data to obtain estimates of age, gender, race and body mass index (BMI). We developed an algorithm to match countries without prevalence estimates with countries from which OSA epidemiological studies exist. The situation was complicated given the variable age of the existing studies, the differences in technology used (e.g., nasal pressure vs thermistor), the changing scoring criteria (e.g., AASM 1999 vs. 2007 vs. 2012) and other sources of variability. Among the 40 countries in the Americas there were age, gender, race and BMI data available for 38. We estimated an Americas’ adult OSA prevalence of approximately 170 million people (37.0%) based on the AASM 2012 criteria using a somewhat conservative approach to our estimates. Furthermore, we estimate 81 million adults in the Americas (17.7%) suffer from moderate to severe OSA based on an apnea hypopnea index ≥ 15/h. The nations with the greatest burden of OSA are the United States (54 million), Brazil (49 million), and Colombia (11 million). This large burden of disease (170 million with OSA; 81 million with moderate to severe OSA in the Americas) has not been widely appreciated and speaks to the need to leverage new technology to diagnose and treat these patients. Further efforts with advocacy are also required for patients, providers and policy makers to develop strategies regarding how best to address. This study was supported by ResMed Corp.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.005 | 0.002 |
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".