The association between high risk of sleep apnea, comorbidities, and risk of COVID-19: a population-based international harmonized study
Bibliographic record
Abstract
Purpose Obstructive sleep apnea (OSA) may increase the risk of severe COVID-19; however, the level of potential modulation has not yet been established. The objective of the study was to determine the association between high risk of OSA, comorbidities, and increased risk for COVID-19, hospitalization, and intensive care unit (ICU) treatment. Methods We conducted a cross-sectional population-based web survey in adults in 14 countries/regions. The survey included sociodemographic variables and comorbidities. Participants were asked questions about COVID-19, hospitalization, and ICU treatment. Standardized questionnaire (STOP questionnaire for high risk of OSA) was included. Multivariable logistic regression was conducted adjusting for various factors. Results Out of 26,539 respondents, 20,598 (35.4% male) completed the survey. Mean age and BMI of participants were 41.5 +/- 16.0 years and 24.0 +/- 5.0 kg/m2, respectively. The prevalence of physician-diagnosed OSA was 4.1% and high risk of OSA was 9.5%. We found that high risk of OSA (adjusted odds ratio (aOR) 1.72, 95% confidence interval (CI): 1.20, 2.47) and diabetes (aOR 2.07, 95% CI: 1.23, 3.48) were associated with reporting of a COVID-19 diagnosis. High risk for OSA (aOR 2.11, 95% CI: 1.10-4.01), being male (aOR: 2.82, 95% CI: 1.55-5.12), having diabetes (aOR: 3.93, 95% CI: 1.70-9.12), and having depression (aOR: 2.33, 95% CI: 1.15-4.77) were associated with increased risk of hospitalization or ICU treatment. Conclusions Participants at high risk of OSA had increased odds of having COVID-19 and were two times more likely to be hospitalized or treated in ICU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".