Budget impact analysis of adopting primary care-based COPD case detection in the Canadian general population
Bibliographic record
Abstract
Objectives An estimated 70% of Canadians with chronic obstructive pulmonary disease (COPD) remain undiagnosed, representing a critical barrier to early intervention to improve disease outcomes. Emerging evidence suggests that opportunistic primary-care based case detection for COPD is a cost-effective solution. We built on a previous cost-effectiveness analysis by evaluating the budget impact of adopting a case detection programme in the general Canadian population. Methods This study accords with ISPOR best practice guidelines for budget impact analysis. We used a validated whole disease microsimulation model of COPD in the general Canadian population to evaluate eight case detection strategies implemented during routine primary care visits, varying in their patient eligibility criteria and testing technology. We assessed COPD-related healthcare costs from the healthcare payer perspective over a five-year time horizon (2022-2026) with gradual programme uptake from 5% to 25% by 2026. Costs were determined from Canadian studies and updated to 2021 Canadian dollars. Key parameters were varied in one-way sensitivity analysis. Results Compared to no case detection, all strategies resulted in substantial budget expansion. In the most cost-effective scenario at a willingness-to-pay of $50,000/QALY (questionnaire-based testing for all patients ≥40 years), total additional costs were $427 million over five years, with 86% of costs attributed to administering case detection and subsequent diagnostic testing. Furthermore, there were 4.6 million referrals to diagnostic spirometry, 96% of which were false positives. The proportion of individuals with COPD who were diagnosed increased from 30.4% to 37.8% by 2026. Results were most sensitive to case detection uptake in primary care. Conclusions A national primary care-based COPD detection programme will require prioritisation by budget holders and significant additional investment in the availability of diagnostic spirometry. Case detection could be effective for reducing the burden of undiagnosed COPD but will depend on successful uptake of the programme in primary care.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".