Cost-effectiveness of a government rebate program for air cleaners in preventing asthma and related adverse health outcomes
Bibliographic record
Abstract
ABSTRACT Background Wildfire-derived fine particulate matter (PM 2.5 ) is an increasing contributor to air pollution in British Columbia (BC), Canada, and is linked to asthma development and asthma-related adverse outcomes including exacerbations. Portable high-efficiency particulate air (HEPA) cleaners can reduce indoor PM 2.5 , but evidence on their long-term, population-level cost-effectiveness is limited. Methods We developed a monthly, time-varying Markov cohort model (2010–2035; healthcare payer perspective; 1.5% annual discounting; $50,000/QALY willingness-to-pay) for two BC cohorts (age 5 and age 25 at baseline) across 16 Health Service Delivery Areas (HSDAs). The model simulated monthly transitions between health states (well-controlled asthma, not well-controlled asthma, exacerbations, and death) over a 25-year time horizon (2010–2035). The target population was children age 5 and adults aged 25 (separate cohorts). We combined historical total PM 2.5 (2010–2022) from provincial data sources with projected PM 2.5 (2023–2035) combining anthropogenic emissions with wildfire-derived PM 2.5 . Wildfire PM 2.5 projections were calculated by multiplying monthly PM 2.5 averages from 2018–2022 by 0%, 5.5%, and 11% cumulative PM 2.5 increase scenarios, informed by climate modeling. The base-case rebate was $150, and varied rebates between $50 to $200. We assumed continuous use with unit replacement every 5 years and filter replacement every 9 months. We calculated asthma incidence attributable to wildfire PM 2.5 and incremental cost-effectiveness ratios (ICERs) across BC’s 16 Health Service Delivery Areas (HSDAs). We conducted the analysis from a health payer perspective with a 1.5% discount rate and $50,000/QALY willingness to pay threshold. Costs are expressed in 2024 CAD. Results From 2023–2036, wildfire-derived PM 2.5 was associated with 13–14 incident asthma cases per 100,000 person-years annually across BC. Over 25 years, air cleaners prevented 444 moderate exacerbations, 55 emergency department visits, and 42 hospitalizations (combined cohorts), but the base-case program was not cost-effective in any HSDA (ICER range: $149,408–$154,749/QALY). A $50 rebate was cost-effective province-wide and $100 was cost-effective in three HSDAs. Results were most sensitive to concentration-response functions for PM2.5 (incidence, control, exacerbations) and to HEPA air cleaner and asthma care costs. Cost-effectiveness was most sensitive to air cleaner costs and the concentration-response function between PM 2.5 and asthma incidence. Conclusions Wildfire-related PM 2.5 contributes meaningfully to asthma incidence in BC. A universal $150 HEPA rebate program was not cost-effective for primary prevention under base-case assumptions, whereas lower rebates ($50 province-wide, $100 in three HSDAs) may offer better value. Future evaluations should co-benefits across multiple disease outcomes to better support policies to reduce the health impacts of increasing wildfires.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".