Exposure to outdoor air pollution, wildfires, and cancer survival in the United States
Bibliographic record
Abstract
Background Climate change has led to an increase in wildfires, a major source of air pollution, which may be particularly harmful to individuals diagnosed with cancer. The objective of this study was to examine the relationships of air pollution and wildfires with mortality risk among cancer survivors. Methods Surveillance, Epidemiology, and End Results (SEER) cancer registries provided information on 7,051,014 patients diagnosed with cancer from 2000 to 2021 in the United States. Cox regression was used to calculate adjusted hazard ratios (HRs) and 95 % confidence intervals (CIs) for the associations between exposures to particulate matter < 2.5 µm (PM 2.5 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), and wildfires (estimated using high-resolution geospatial datasets) with all-cause and cause-specific mortality risk. Results There were 3,452,593 deaths, including 2,369,364 from cancer, 525,409 from cardiopulmonary, and 557,820 from other causes. Among cancer survivors, higher exposure to PM 2.5 and wildfires (but not NO 2 or O 3 ) were associated with increased risk for all-cause, cancer, and other mortality. The association between PM 2.5 and cancer mortality was stronger in counties more heavily impacted by wildfires (HR per 10 μg/m 3 in counties with ≥ median 0.39 wildfires per year: 1.17, 95 % CI 1.04–1.33) vs. no wildfires (HR 1.06, 95 % CI 0.97–1.15) (p interaction = 0.0064). Conclusions Among patients diagnosed with cancer, PM 2.5 air pollution, particularly in areas heavily impacted by wildfires, is associated with increased risk for mortality.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".