Evaluating the Long-Term Cost-Effectiveness of a Government Rebate Program for Air Cleaners in Preventing Asthma in British Columbia, Canada
Bibliographic record
Abstract
Abstract RATIONALE: With the increasing frequency and severity of wildfires across North America, exposure to fine particulate matter (PM2.5) has become a growing health concern due to its links to asthma incidence, poor asthma control, and exacerbations. Population-level mitigation measures are necessary to reduce these asthma-related health impacts. METHODS: We developed a time-varying Markov model to evaluate the cost-effectiveness of air cleaner rebates aimed at reducing asthma burden in the general population of British Columbia (BC), Canada, where the prevalence of asthma is 12.7%. We used published concentration-response functions and a 31% infiltration ratio for air cleaners to evaluate the impact of reduced PM2.5 exposure on asthma incidence, control, moderate-severe exacerbations, and death over a 25-year time horizon (2012-2036). Monthly historical ambient PM2.5 concentrations (2012-2022) were obtained using the Canadian Optimized Statistical Smoke Exposure Model. Projected monthly PM2.5 (2023-2036) were determined from chemical transport models and calculated as the sum of PM2.5 from anthropogenic emissions and the fraction of total PM2.5 attributable to wildfires (2018-2023 average), adjusted by climate scaling factors (0%, 4.2% and 8.4%, separate scenarios), representing possible wildfire PM2.5 increases by 2036. We calculated the incremental cost-effectiveness ratio (ICER) for government rebates covering 100% of the cost of air cleaners and replacement cost every 5 years. We assumed air cleaners would be operated continuously in households with children ≥5 years and adults ≥35 years (separate cohorts). We reported results in 16 Health Service Delivery Areas (HSDA). RESULTS: The ICER for a full government rebate on air cleaners varied from $158,219 per Quality-Adjusted Life Year (QALY) with a 0% scaling factor to $126,370 per QALY with an 8.4% scaling factor. Kootenay Boundary was the only HSDA where the ICER ($40,101/QALY, $42,190/QALY, and $45,943/QALY, at 0%, 4.2%, and 8.4% scaling factors, respectively) remained under the $50,000/QALY willingness-to-pay threshold. Province-wide, the program would prevent 18,432 new cases of asthma, 16,671 moderate exacerbations, 2,795 emergency room visits, and 1,723 hospitalizations over 25 years. In comparison, the child cohort demonstrated better cost-effectiveness and cases prevented ($107,102/QALY, 10,697 cases prevented) compared to the adult cohort ($148,380/QALY, 7,735 cases prevented). CONCLUSIONS: Government rebates for air cleaners in the general population may be a cost-effective strategy in reducing asthma incidence and related health complications in BC, particularly in regions with high wildfire activity. Our study highlights the potential of preventive interventions in addressing environmental challenges.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".