Estimating the Health and Economic Impact of Improved Management in Prevalent Chronic Obstructive Pulmonary Disease Populations in England, Germany, Canada, and Japan: A Modelling Study
Bibliographic record
Abstract
Introduction: COPD is a leading cause of morbidity and mortality globally. Management is complex and costly. Although international quality standards for diagnosis and management exist, opportunities remain to improve outcomes, especially in reducing avoidable hospitalisations. Objective: To estimate the potential health and economic impact of improved adherence to guideline-recommended care for prevalent, on-treatment COPD populations in four high-income settings. Methods: A disease simulation model was developed to evaluate the impact of theoretical improvements to COPD management, comparing outcomes for usual care and policy scenarios for interventions that reduce avoidable hospitalisations: 1) increased attendance (50% vs 31-38%) of early follow-up review after severe exacerbation hospitalisation; 2) increased access (30% vs 5-10%) to an integrated disease management (IDM) programme that provides guideline adherent care. Results: For cohorts of 100,000 patients, Policy 1 yielded additional life years (England: 523; Germany: 759; Canada: 1316; Japan: 512) and lifetime cost savings (-£2.89 million; -€6.58 million; -$40.08 million; -¥735.58 million). For Policy 2, additional life years (2299; 3619; 3656) and higher lifetime total costs (£38.15 million; €35.58 million; ¥1091.53 million) were estimated in England, Germany and Japan, and additional life years (4299) and cost savings (-$20.52 million) in Canada. Scenarios found that the cost impact depended on the modelled intervention effect size. Conclusion: Interventions that reduce avoidable hospitalisations are estimated to improve survival and may generate cost savings. This study provides evidence on the theoretical impact of policies to improve COPD care and highlights priority areas for further research to support evidence-based policy decisions.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".