Forest Fire Smoke Exposure and Cardiorespiratory Mortality in British Columbia, Canada from 2003-2012
Bibliographic record
Abstract
Forest Fire Smoke Exposure and Cardiorespiratory Mortality in British Columbia, Canada from 2003-2012Abstract Number:2787 Sarah Henderson* and Angela Yao Sarah Henderson* BC Centre for Disease Control, Canada, E-mail Address: [email protected] and Angela Yao BC Centre for Disease Control, Canada, E-mail Address: [email protected] AbstractBackground: Smoke from forest fires can cause some of the poorest air quality that many populations will ever experience. There have been several important advancements in forest fire smoke (FFS) epidemiology over the past decade, including use of models and remote sensing data for improved exposure assessment and use of administrative health data for population-based research. However, only a handful of studies have evaluated risk of mortality because FFS exposure is typically sporadic, and rarely affects large populations for long enough to support precise statistical analyses. Here we use a spatially resolved model and ten years of mortality records to assess the impacts of FFS over the entire population of British Columbia, Canada.Methods: Daily average PM2.5 exposure was estimated at a resolution of 5km for 2003- 2012 using our previously published model. All summertime deaths were geocoded according to their residential addresses. We used a case-crossover design to compare the effects of estimated PM2.5 on fire days with the effects on non-fire days, which were defined used remote sensing data. Odds ratios (OR) were calculated for all-cause mortality, and mortality from specific cardiovascular and respiratory causes. All analyses were stratified by urban and rural areas.Results: A 10 ug/m3 increase in estimated fire day PM2.5 was associated with increased odds of mortality from all cardiovascular causes (1.05; 1.00 – 1.10), all respiratory causes (1.09; 1.01 – 1.17), and stroke (1.13; 1.03 – 1.24). The effects were stronger in rural areas than in urban areas, and they were not detected on non-fire days. Discussion: By combining a spatially resolved exposure model with administrative data for an entire population over ten years we were able to detect statistically significant effects of FFS on respiratory and cardiovascular mortality. The cardiovascular mortality was driven by stroke, and this important public health finding has not been previously reported.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".