Asthma medication usage after environmental exposure to wildfire smoke: A systematic review
Bibliographic record
Abstract
Asthma is a chronic respiratory condition exacerbated by exposure to particulate air pollution. Smoke from landscape fires has been associated with increased mortality, asthma-related admissions to emergency and other hospital departments, and uptake in primary care services. With climate change and more frequent landscape fires, healthcare systems must prepare for disaster, including surges in asthma medication demand. Past reviews have not resolved the direction and magnitude of the association between PM 2.5 exposure during landscape fires and asthma medication use. The aim of this review was to investigate the relationship between exposure to landscape fire smoke and the use of asthma medications. We conducted a systematic review of PubMed, Scopus, and Web of Science, identifying peer-reviewed articles that examined asthma medication usage following environmental exposure to landscape fire smoke. After a full-text review, we identified twelve articles, three from Canada, three from the USA and six from Australia, with five being retrospective cohort studies. Despite differences in study design, outcome and exposure assessment, the included studies reported a consistent increase in asthma medication use after exposure to wildfires. There is consistent evidence that exposure to wildfire smoke is associated with an increase in the use of reliever medications, particularly salbutamol. Increases in other asthma management medications were also consistently identified. Increases in demand for asthma medications after exposure to wildfire smoke highlight the urgent need to address the growing frequency and intensity of wildfires driven by climate change.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".