Association of air quality during forest fire season with respiratory emergency department visits in Vancouver, British Columbia
Bibliographic record
Abstract
Climate change has been deemed the biggest global health threat of the 21st century. One consequence of climate change is the increasing frequency and severity of forest fires. Smoke from wildfires has the ability to negatively impact air quality over large distances. The aim of this study was to examine the association that air quality had on emergency department visits for cardiac, respiratory and psychiatric/behavioral health chief complaints during forest fire season in Vancouver, British Columbia. The study period was January 1, 2009 – December 31, 2019. Forest fire season was defined as April 1- September 30. Air quality (measured by PM2.5 in ug/m3) was obtained from the Vancouver International Airport (YVR) Air Quality station. Emergency department visit data (CEDIS triage complaint) was acquired from a regional emergency department database. A generalized linear mixed model with Poisson link function was used to determine the relative risk (as a percentage) for respiratory, cardiac and psychiatric/behavioral health CEDIS triage complaints associated with a 10 unit increase in PM2.5. PM2.5 during forest fire season was significantly associated with emergency department visits for respiratory chief complaints. For every 10 ug/m3 increase in PM2.5, there was a 4.61% (95% CI: 3.07, 6.17) increase in relative risk of respiratory chief complaints presenting to emergency departments. No association was found between PM2.5 and cardiac or psychiatric/behavioral health chief complaints during forest fire season or non-forest fire season. During non-forest fire season, PM2.5 was found to be negatively associated with respiratory (-3.57, 95% CI: -5.44, -1.66) and cardiac chief complaints (-2.77, 95% CI: -4.16, -1.47). Our results indicate a probable association between air quality during forest fire season and emergency department visits for respiratory chief complaints. This provides further illustration of the widespread impact of climate change, and underscores the importance of efforts to address it.
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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 | 0.003 | 0.000 |
| 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".