The Impact of Time Scale on the Cost-Effectiveness of Air Cleaners for Reducing Exposure to Air Pollution From Wildfires
Bibliographic record
Abstract
Abstract RATIONALE: Concentration response functions (CRF) quantify the association between air pollution concentration and health outcomes and are an essential input in economic models used to evaluate air quality interventions for asthma control. However, there is considerable uncertainty in the correct value and shape of the CRF for asthma outcomes across a range of air pollution concentrations and modelers must choose from among multiple reasonable options. Furthermore, uncertainty in the CRF can be magnified by its interaction with the model time scale, i.e., whether exposure is averaged daily or monthly. This means modeling decisions could impact the estimated cost-effectiveness of air quality interventions (e.g., air cleaners) for asthma control, influencing patient access to these interventions. METHODS: We conducted a cost-effectiveness analysis (CEA) of indoor air cleaners, holding the CRF constant but changing the time scale for air pollution measurement. The CEA used a Markov model with four health states for well-controlled, partly controlled, uncontrolled asthma symptoms, and death. We used a CRF of 1.06 additional salbutamol inhalers dispensed per 10 µg/m³ increase in PM2.5, derived from a log-linear regression of asthma patients in British Columbia (BC), Canada. We incorporated daily data on PM2.5 concentration from 2018-2022, determined in 16 health service delivery areas (HSDA) in BC, and averaged over daily, weekly and monthly time periods. We evaluated the incremental cost-effectiveness ratio (ICER) of government rebates for air cleaners (vs. no rebates) over a 5-year period in each HSDA. RESULTS: Modeling air pollution on a daily time scale rather than weekly or monthly changed cost-effectiveness results in all 16 HSDAs, indicating a higher value of air cleaners with daily PM2.5 (Figure 1). The monthly to daily difference in ICERs was smallest (-$110/QALY) in the HSDA with the lowest daily mean PM2.5 (7.1 µg/m³, SD 1.9) and largest (-$6,783/QALY) in the HSDA with the highest daily mean PM2.5 (11.2 µg/m³, SD 23.2). CONCLUSIONS: Both the choice of CRF and model time scale influenced the estimated cost-effectiveness of air cleaners for improving asthma control, changing the estimated cost per QALY by approximately $7000 in the region most affected by PM2.5. As there is no obvious ‘correct’ CRF or model time scale, these modeling decisions reflect important sources of uncertainty around the value of air cleaners. Modelers should be transparent with the public and decision makers about this uncertainty when informing policy discussions around patient access to air cleaners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".