Linking air pollution and administrative health databases to examine health effects of wildfire smoke exposure in Calgary, Canada in 2015
Bibliographic record
Abstract
IntroductionWildfires are increasing in frequency and severity due to climate change. Fine particulate matter (PM2.5) in wildfire smoke is an important indicator of health effects of most combustion sources. The evidence based on adverse health impacts of PM2.5 from wildfire smoke are increasingly being studied but some gaps still remain. Objectives and ApproachWe examined the association of PM2.5 from Pacific Northwest wildfires with multiple respiratory and cardiovascular morbidity events related physician and emergency department visits, hospital admissions, health-link calls and medications dispensed among Calgary city population from August 1 – September 30, 2015. Physician billing claims, discharge abstract, Pharmaceutical Information Network, Health-link calls databases were linked with the air quality monitoring information database. Quasi-Poisson regression model for time lags of zero to five days and a three-day moving average, and conditional logistic regression model was used with adjustment for air pollutants and meteorological variables. Age and disease-specific stratified analyses were performed. ResultsCompared to the pre-wildfire period (Aug 1 – Aug 23), a 10 µg/m3 increase in PM2.5 increased the risk for respiratory physician visits by 54.8% (95% CI: 41.6% - 69.3%) and 32.6% (95% CI: 10.4% - 58.9%) in the respective post (Sep 1- Sep 30) and during (Aug 24 – Aug 31) wildfire periods. The strongest association of PM2.5 with respiratory physician visits was observed for children aged 0-9 years in the post-wildfire period (Relative Risk [RR] = 2.11, 95% CI: 1.86 - 2.39) compared to during wildfire period (RR = 1.56, 95% CI: 1.21 - 2.02) and was consistent for asthma, acute bronchitis and acute respiratory infection. Statistically significant effects of PM2.5 on cardiovascular hospitalizations, ED and physician visits were not observed during the wildfire period. Conclusion/ImplicationsWildfire-related PM2.5 led to increased physician visits due to respiratory morbidity during and after the wildfires, particularly for asthma, acute bronchitis and acute respiratory infections in children. The absence of cardiovascular health impact in general population during wildfires provides useful information for targeted public health messaging during adverse air quality events.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".