1310 Receipt of Glucagon-Like Peptide-1 Receptor Agonists Among Rural Patients with Obstructive Sleep Apnea and Excess Weight
Bibliographic record
Abstract
Abstract Introduction Patients living in rural areas have a greater burden of obstructive sleep apnea (OSA) and other weight-related comorbidities. Simultaneously, highly effective services for weight management (e.g., bariatric surgery) are resource intensive and limited to urban centers. New Glucagon-Like Peptide-1 Receptor Agonists (GLP-1RAs) are highly effective for weight loss, decrease OSA severity, and comprise a scalable solution to potentially meet the needs of rural areas. We aimed to test the association of rurality with the initiation of GLP-1RAs among patients with OSA and excess weight. Methods We used electronic health record data from the Veterans Health Administration to identify patients meeting the following criteria: 1) sleep study between October 1, 2017 and May 1, 2023, 2) diagnosis of OSA, 3) body mass index ≥ 27 kg/m2, and 4) participation in a lifestyle-based weight management program. We defined rurality by Rural Urban Commuting Area Codes, census tract-based codes that we collapsed into a binary variable of rural vs urban. The outcome was the receipt of a GLP-1RA approved for chronic weight loss (liraglutide, semaglutide, or tirzepatide) within one year of meeting inclusion criteria. We performed a mixed effects logistic regression accounting for patient and site level factors including demographics, comorbidities, and drive time to care. Results 68,862 patients met the inclusion criteria, and 17,929 (26.0%) lived in rural areas. Overall, 8.7% of patients living in rural areas received a GLP-1RA, relative to 7.8% of urban peers. However, after accounting for patient and site level confounders, patients living in rural areas had ~10% lower odds of receipt of a GLP-1RA (OR 0.91, 95%CI 0.83-0.98). This trend remained similar in sensitivity analyses which 1) stratify data by diabetes diagnoses and 2) considered receipt of other weight management medications. Conclusion Among patients with OSA and excess weight, after adjustment for confounding variables, those living in rural areas appear to be less likely to receive GLP-1RAs relative to urban peers. Gaps in the delivery of these promising medications may potentiate disparities in health-related outcomes. New strategies are needed to overcome barriers to the delivery of GLP-1RAs to patients with OSA and excess weight. Support (if any)
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".