Budget impact analysis of adopting primary care–based case detection of chronic obstructive pulmonary disease in the Canadian general population
Bibliographic record
Abstract
BACKGROUND: An estimated 70% of Canadians with chronic obstructive pulmonary disease (COPD) have not received a diagnosis, creating a barrier to early intervention, and there is growing interest in the value of primary care-based opportunistic case detection for COPD. We sought to build on a previous cost-effectiveness analysis by evaluating the budget impact of adopting COPD case detection in the Canadian general population. METHODS: We used a validated discrete-event microsimulation model of COPD in the Canadian general population aged 40 years and older to assess the costs of implementing 8 primary care-based case detection strategies over 5 years (2022-2026) from the health care payer perspective. Strategies varied in eligibility criteria (based on age, symptoms or smoking history) and testing technology (COPD Diagnostic Questionnaire [CDQ] or screening spirometry). Costs were determined from Canadian studies and converted to 2021 Canadian dollars. Key parameters were varied in one-way sensitivity analysis. RESULTS: All strategies resulted in higher total costs compared with routine diagnosis. The most cost-effective scenario (the CDQ for all patients) had an associated total budget expansion of $423 million, with administering case detection and subsequent diagnostic spirometry accounting for 86% of costs. This strategy increased the proportion of individuals diagnosed with COPD from 30.4% to 37.8%, and resulted in 4.6 million referrals to diagnostic spirometry. Results were most sensitive to uptake in primary care. INTERPRETATION: Adopting a national COPD case detection program would be an effective method for increasing diagnosis of COPD, dependent on successful uptake. However, it will require prioritisation by budget holders and substantial additional investment to improve access to diagnostic spirometry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".