Meta-analysis of the association between low concentration PM <sub>2.5</sub> and cardiovascular mortality in the United States and Canada
Bibliographic record
Abstract
Objectives The adverse effects of fine particulate matter (PM2.5), including cardiovascular outcomes, are well established. This review and meta-analysis investigates the association between long-term exposure to low concentration PM2.5 (<12 µg/m3) and CVD mortality in U.S. and Canadian populations.Methods We conducted a literature search and completed random effect meta-analyses.Results Twenty-four studies were reviewed, with 12 from each of the U.S. and Canada. Fifteen of eighteen studies that reported hazard ratios (HRs) for total CVD mortality reported statistically significant positive associations with low concentration PM2.5. For cause-specific CVD mortality, more consistent results were shown for ischemic heart disease (IHD) mortality, with all eleven studies reporting statistically significant associations (HR = 1.09 to 2.48). Only three of 12 studies evaluating cerebrovascular mortality reported statistically significant associations (HR = 1.10 to 1.27). Studies that restricted analyses to participants with mean exposures <12 µg/m3 found statistically significant associations between PM2.5 and at least some of the CVD mortality outcomes of interest. However, the shape of the concentration-response functions varied widely. Only six studies controlled for at least one additional air pollutant, and multi-pollutant models generally showed an attenuated impact of PM2.5. Despite existing gaps in understanding the association between low concentrations of PM2.5 and cardiovascular mortality, this review highlights the critical importance of ongoing efforts to improve air quality for public health benefits.Conclusions Continued focus on understanding the shape of the concentration-response function for PM2.5, the impact of co-pollutants on observed effects, and how particle composition may impact effect estimates, is recommended.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.037 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".