Impact of the 2023 wildfire smoke episodes in Ontario, Canada, on asthma and other health outcomes: an interrupted time-series analysis
Bibliographic record
Abstract
BACKGROUND: During the 2023 wildfire season, Ontario, Canada, had unprecedented wildfire smoke, but the health impact on the population is unknown. We aimed to quantify the acute impact of the wildfire smoke on respiratory and cardiovascular outcomes across Ontario. METHODS: We conducted a quasi-experimental study by leveraging the timing of 2 consecutive wildfire smoke episodes in June 2023. Heavy wildfire smoke blanketed much of Ontario on 2 occasions, in early June and again in late June, causing severely degraded daily air quality. Following the epidemiologic triangulation framework, we collected health data on emergency department visits for 4 outcomes (asthma-related causes, other respiratory causes, ischemic heart disease, and non-cardiorespiratory causes) from Ontario's real-time syndromic surveillance system and the National Ambulatory Care Reporting System. We also employed different epidemiologic methodologies, including interrupted time-series and case-crossover analyses. RESULTS: After the initial heavy wildfire smoke in early June 2023, daily asthma-related visits increased substantially across Ontario, peaking at a 23.6% increase (95% confidence interval 13.2%-34.9%) at a 1-day lag and lasting up to a lag of 5 days after the start of the smoke episode. The later episode of heavy smoke, despite causing higher exposures, had a reduced effect on asthma-related visits. We did not detect any effect on other outcomes in either episode. These findings were consistent across different methodologies and data sources. Post hoc analysis revealed that asthma-related visits were briefly elevated after the wildfire smoke among children (40% higher), but we observed a more sustained effect among adults (48% higher, lasting 1 week). INTERPRETATION: The 2023 wildfires substantially increased asthma-related emergency department visits in Ontario, with age and timing of exposure being important factors influencing the impact. As wildfires emerge as one of the fastest-growing environmental risk factors globally, future research should identify and evaluate measures to effectively mitigate the acute health impacts of wildfire smoke.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".