Cost-effectiveness of integrated disease management for high risk, exacerbation prone, patients with chronic obstructive pulmonary disease in a primary care setting
Bibliographic record
Abstract
Abstract Background We evaluate the cost-effectiveness of the ‘Best Care’ integrated disease management (IDM) program for high risk, exacerbation prone, patients with chronic obstructive pulmonary disease (COPD) compared to usual care (UC) within a primary care setting from the perspective of a publicly funded health system (i.e., Ontario, Canada). Methods We conducted a model-based, cost-utility analysis using a Markov model with expected values of costs and outcomes derived from a Monte-Carlo Simulation with 5000 replications. The target population included patients started in GOLD II with a starting age of 68 years in the trial-based analysis. Key input parameters were based on a randomized control trial of 143 patients (i.e., UC (n = 73) versus IDM program (n = 70)). Results were shown as incremental cost per quality-adjusted life year (QALY) gained. Results The IDM program for high risk, exacerbation prone, patients is dominant in comparison with the UC group. After one year, the IDM program demonstrated cost savings and improved QALYs (i.e., UC was dominated by IDM) with a positive net-benefit of $5360 (95% CI: ($5175, $5546) based on a willingness to pay of $50,000 (CAN) per QALY. Conclusions This study demonstrates that the IDM intervention for patients with COPD in a primary care setting is cost-effective in comparison to the standard of care. By demonstrating the cost-effectiveness of IDM, we confirm that investment in the delivery of evidence based best practices in primary care delivers better patient outcomes at a lower cost than UC.
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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 | 0.011 |
| 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) | 0.002 | 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".