Regional variations and trends in the use of maintenance inhaled medications in chronic obstructive pulmonary disease
Bibliographic record
Abstract
Background: Maintenance inhaled medications are the cornerstone of chronic obstructive pulmonary disease (COPD) management. However, our understanding of regional variation in medication use in COPD remains limited. Objectives: We examined temporal and geographical trends in COPD pharmacotherapy in British Columbia, Canada. Methods: Using administrative health databases (2010-2020), we created a retrospective cohort of COPD patients aged ≥35 years meeting a validated case definition. Geographic regions were Health Services Delivery Areas, and the primary outcome was annual proportion of patients who used COPD-related maintenance inhaled medications, overall and by drug class. Single therapies consisted of long-acting muscarinic antagonists (LAMA), long-acting beta-agonists (LABA), and inhaled corticosteroids (ICS), whereas combination therapies included single or separate inhalers with ≥14 days of overlapping. We used generalized linear models to analyze medication use trends by region. Results: Over 11 years, 189,204 COPD patients used maintenance inhaled medications (51% female; mean age: 68). The proportion of users declined 2% annually (p<0.01). ICS was the most common single inhaler therapy (33%) but declined the fastest (10%/year), while LAMA use (19%) increased 2%/year. Among combination therapies, ICS+LABA was most common (39%) but decreased by 3%/year, whereas LAMA+LABA (6%) and triple therapy (ICS+LAMA+LABA, 15%) rose by 44% and 4% annually. Regional variability was significant, with the highest-use region showing a 1.5 times higher use than the lowest. Conclusion: There was a declining trend in inhaled medication use in COPD patients, varying across regions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".