Cost‐Effectiveness of Combination Therapy for Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
BACKGROUND: There is evidence that combination therapy (CT) in the form of long-acting beta(2)-agonists (LABAs) and inhaled corticosteroids can improve lung function for patients with chronic obstructive pulmonary disease (COPD). OBJECTIVE: To determine the cost-effectiveness of using CT in none, all or a selected group of COPD patients. METHODS: A Markov model was designed to compare four treatment strategies: no use of CT regardless of COPD severity (patients receive LABA only); use of CT in patients with stage 3 disease only (forced expiratory volume in 1 s [FEV(1)] less than 35% of predicted); use of CT in patients with stages 2 and 3 disease only (FEV(1) less than 50% of predicted); and use of CT in all patients regardless of severity of COPD. Estimates of mortality, exacerbation and disease progression rates, quality- adjusted life years (QALYs) and costs were derived from the literature. Three-year and lifetime time horizons were used. The analysis was conducted from a health systems perspective. RESULTS: CT was associated with a cost of $39,000 per QALY if given to patients with stage 3 disease, $47,500 per QALY if given to patients with stages 2 and 3 disease, and $450,333 per QALY if given to all COPD patients. Results were robust to various assumptions tested in a Monte Carlo simulation. CONCLUSION: Providing CT for COPD patients in stage 2 or 3 disease is cost-effective. The message to family physicians and specialists is that as FEV(1) worsens and reaches 50% of predicted values, CT is recommended.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".