20-year trends in excess costs of COPD
Bibliographic record
Abstract
Background Several major risk factors for COPD, such as population ageing, smoking rates and air pollution levels, are rapidly changing, causing inevitable changes in the population burden of COPD. We determined the excess direct costs of COPD and their trend from 2001 to 2020. Methods Using administrative health data from British Columbia, Canada, we created a retrospective matched cohort of physician-diagnosed COPD patients and non-COPD individuals. Excess direct medical costs (in 2020 Canadian dollars (CAD)) were estimated by analysing hospital records, outpatient services, medications and community care services. Comorbidity classes were assessed using International Classification of Diseases codes. Excess COPD costs were estimated as the adjusted difference in direct medical costs between the COPD and non-COPD cohorts. Results There were 208 554 and 404 703 individuals in the COPD and non-COPD cohorts, respectively (47.8% female; mean baseline age 69.1 and 68.2 years, respectively). Direct medical costs for COPD were CAD 9224 per patient-year compared to CAD 3396 per patient-year for non-COPD, giving rise to excess costs of CAD 5828 (95% CI 5759–5897) per patient-year. Excess costs increased by 48% over the study period. Excess costs due to comorbidities were CAD 3588 (95% CI 3554–3622) per patient-year, with cardiovascular-related conditions alone exceeding the costs attributed to COPD (CAD 1375versus904 per patient-year). Conclusions Despite multifaceted prevention and management initiatives, COPD-related economic burden is increasing, with the majority of costs due to comorbid conditions. Rising per-patient costs, combined with the flat or increasing prevalence of COPD in many jurisdictions, indicates a significant increase in COPD burden.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".