Canadian Wildfires of 2023 and Risk of Mortality and Hospitalization among ESKD Patients
Bibliographic record
Abstract
Background: Climate change driven droughts are increasing frequency and intensity of wildfires, a significant source of air pollution. Smoke plumes from the 2023 Canadian wildfires travelled long distances and negatively impacted air quality across large swaths of eastern US. We investigated the association between exposure to 2023 Canadian wildfire-related air pollutants and risk of mortality and hospitalization among hemodialysis patients in New England, the Mid-Atlantic, and the Midwest U.S. Methods: The study population includes the end stage kidney disease patients (N=52,995) receiving hemodialysis treatment at Fresenius Kidney Care clinics located in New England, the Mid-Atlantic, and the Midwest U.S. during June-July 2023. Daily number of all-cause deaths, all-cause hospitalizations, respiratory disease hospitalizations, and cardiovascular disease hospitalizations were counted for each hemodialysis clinic. Presence and absence of wildfire smoke plume and wildfire-related fine particulate matter (PM2.5) concentration were assessed using both satellite-derived smoke polygons and ground-based PM2.5 monitors. We constructed a retrospective observational study using a time-stratified case-crossover analysis with a conditional quasi-Poisson model to investigate the risk of mortality and hospitalization associated with exposures to wildfire-related air pollutants. Results: Canadian wildfires of 2023 significantly increased air pollution level across the dialysis clinics in the study area, with the highest daily wildfire-related PM2.5 concentration reaching 251.1µg/m3. The presence of wildfire smoke plume was associated with an 18% increase in risk of same day (lag0) all-cause mortality (rate ratio [RR]:1.18; 95% confidence interval [CI], 1.13-1.24) and a 3% increase in risk of all-cause hospitalization (RR:1.03; 95% CI, 1.00-1.07). A 10-μg/m3 increase in wildfire-related PM2.5 was associated with a 139% increase in same day all-cause mortality (RR: 2.39; 95% CI, 1.79-3.18), and a 33% increase in all-cause hospitalization (RR:1.33; 95% CI, 1.10-1.62). Conclusion: Our data suggest that air pollution from the 2023 Canadian wildfires resulted in increased risk of mortality and hospitalization among hemodialysis patients in New England, the Mid-Atlantic, and the Midwest U.S.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".