High-Efficiency Particulate Air Filters for Preventing Wildfire-related Asthma Complications: A Cost-Effectiveness Study
Bibliographic record
Abstract
Abstract Rationale Air pollution caused by wildfire smoke is linked to adverse health outcomes, especially for people living with asthma. Objectives To evaluate whether government rebates for high-efficiency particulate air (HEPA) filters, which reduce concentrations of smoke particles indoors, are cost effective in managing asthma and preventing exacerbations in British Columbia (BC), Canada. Methods We used a Markov model to analyze health states for asthma control, exacerbation severity, and death over a retrospective time horizon of 5 years (2018–2022). Concentrations of wildfire smoke–derived particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) from the Canadian Optimized Statistical Smoke Exposure Model and relevant literature informed the model. The base-case analysis assumed continuous use of a HEPA filter. Costs and quality-adjusted life-years (QALYs) resulting from varying rebates were computed for each Health Service Delivery Area (HSDA). Measurements and Main Results In the base-case analysis, HEPA filter use resulted in increased costs of $83.34 (SE, $1.03) and increased QALYs of 0.0011 (SE, 0.0001) per person. The average incremental cost-effectiveness ratio among BC HSDAs was $74,652/QALY (SE, $3,517), with incremental cost-effectiveness ratios ranging from $40,509 to $89,206 per QALY in HSDAs. Across the province, the intervention was projected to prevent 4,418 exacerbations requiring systemic corticosteroids, 643 emergency department visits, and 425 hospitalizations during the 5-year time horizon. A full rebate was cost effective in 1 of the 16 HSDAs across BC. The probability of cost-effectiveness ranged from 0.1% to 74.8% across HSDAs. A $100 rebate was cost effective in most HSDAs. Conclusions The cost-effectiveness of HEPA filters in managing wildfire smoke–related asthma issues in BC varies by region. Government rebates up to two-thirds of the filter cost are generally cost effective, with a full rebate being cost effective only in Kootenay Boundary.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".