Long-term Exposure to Oxidant Gases and Mortality: Effect Modification by PM2.5 Transition Metals and Oxidative Potential
Bibliographic record
Abstract
BACKGROUND: Populations are simultaneously exposed to outdoor concentrations of oxidant gases (i.e., O 3 and NO 2 ) and fine particulate air pollution (PM 2.5 ). Since oxidative stress is thought to be an important mechanism explaining air pollution health effects, the adverse health impacts of oxidant gases may be greater in locations where PM 2.5 is more capable of causing oxidative stress. METHODS: We conducted a cohort study of 2 million adults in Canada between 2001 and 2016 living within 10 km of ground-level monitoring sites for outdoor PM 2.5 components and oxidative potential. O x exposures (i.e., the redox-weighted average of O 3 and NO 2 ) were estimated using a combination of chemical transport models, land use regression models, and ground-level data. Cox proportional hazards models were used to estimate associations between 3-year moving average O x and mortality outcomes across strata of transition metals and sulfur in PM 2.5 and three measures of PM 2.5 oxidative potential adjusting for possible confounding factors. RESULTS: Associations between O x and mortality were consistently stronger in regions with elevated PM 2.5 transition metal/sulfur content and oxidative potential. For example, each interquartile increase (6.27 ppb) in O x was associated with a 14.9% (95% CI = 13.0, 16.9) increased risk of nonaccidental mortality in locations with glutathione-related oxidative potential (OP GSH ) above the median whereas a 2.50% (95% CI = 0.600, 4.40) increase was observed in regions with OP GSH levels below the median (interaction P value <0.001). CONCLUSION: Spatial variations in PM 2.5 composition and oxidative potential may contribute to heterogeneity in the observed health impacts of long-term exposures to oxidant gases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".