Understanding the joint impacts of fine particulate matter concentration and composition on the incidence and mortality of cardiovascular disease:a component-adjusted approach
Bibliographic record
Abstract
Background: Past health impact assessments of ambient fine particles (PM2.5) have generally considered mass concentration only, despite PM2.5 is a heterogeneous mixture. Given constant changes in the concentration and the composition of atmospheric aerosol, uncertainty exists as to whether the current focus on PM2.5 mass or individual components may fully characterize the health burden of PM2.5. Methods: We proposed a component-adjusted method that jointly estimates the health impacts of PM2.5 and its major components while allowing for a potential nonlinear PM2.5-outcome relationship. Using this method, we quantified the effects of PM2.5 on the risks of developing acute myocardial infarction (AMI) and dying from cardiovascular causes in comparison to three traditional approaches in the entire adult population across Ontario, Canada. Results: We observed that PM2.5 was positively associated with AMI incidence and cardiovascular mortality with all four methods. Comparing to the traditional approaches, however, the new component-adjusted approach demonstrated a significant improvement in explaining the health impacts of PM2.5, especially in the presence of a nonlinear PM2.5-outcome relationship. Using the new approach, we found that the effects of PM2.5 on AMI incidence and cardiovascular mortality may be 10% to 27% higher than what would be estimated from the conventional approaches examining PM2.5 alone. Conclusions: We showed that future research on the health effects of PM2.5 could benefit from integrating information about the relative distributions of major components into health risk assessments. The new approach we proposed can provide superior predictive power and a refined understanding of the health effects of PM2.5 compared with a range of alternative modeling approaches.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".