Association between long-term exposure to wildfire-related PM2.5 and mortality: A longitudinal analysis of the UK Biobank
Bibliographic record
Abstract
Little is known about the associations between long-term exposure to wildfire-related fine particulate matter (PM2.5) and mortality. We aimed to explore theses associations using the data from the UK Biobank cohort. Long-term wildfire-related PM2.5 exposure was defined as the 3-year cumulative concentrations of wildfire-related PM2.5 within a 10-km buffer surrounding the residential address for each individual. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using the time-varying Cox regression model. We included 492,394 participants aged between 38 and 73 years. We found that after adjusting for potential covariates, a 10 μg/m3 increase of wildfire-related PM2.5 exposure was associated with a 0.4% higher risk of all-cause mortality (HR = 1.004 [95% CI: 1.001, 1.006]) and nonaccidental mortality (HR = 1.004 [95% CI: 1.002, 1.006]), and a 0.5% higher risk of neoplasm mortality (HR = 1.005 [95% CI: 1.002, 1.008]). However, no significant associations were observed between wildfire-related PM2.5 exposure and mortality from cardiovascular, respiratory, and mental diseases. Additionally, no significant modification effects of a series of modifiers were observed. Targeted health protection strategies should be adopted in response to wildfire-related PM2.5 exposure, in order to reduce the risk of premature 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".