Association between PM <sub>10</sub> exposure and risk of myocardial infarction in adults: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Air pollution has several negative health effects. Particulate matter (PM) is a pollutant that is often linked to health adversities. PM 2.5 (PM with an aerodynamic diameter of ≤2.5μm) exposure has been associated with negative cardiovascular (CV) outcomes. However, the impact of PM 10 (PM with an aerodynamic diameter of ≤10μm) exposure is often overlooked due to its limited ability to pass the alveolar barrier. This study aims to assess the association between PM 10 exposure and risk of myocardial infarction (MI) amongst adults (≥18 years of age) as this has been poorly studied. Methods The study protocol was published on PROSPERO (CRD42023409796) on March 31, 2023. Literature searches were conducted on 4 databases (OVID Medline, Embase, CINAHL, and Web of Science) for studies looking at associations between PM and MI. English studies from all time periods were assessed. Studies selected for review were time-series, case-crossover, and cohort studies which investigated the risk of MI as an outcome upon PM 10 exposure. The quality of evidence was assessed using Cochrane’s GRADE approach. Data for different risk outcomes (risk ratio (RR), odds ratio (OR), hazard ratio (HR)) and 3 lags was meta-analyzed using an inverse variance statistical analysis using a random effects model. The pooled effect sizes and the 95% confidence intervals (CIs) were reported in forest plots. Results Among the 1,099 studies identified, 41 were included for review and 23 were deemed eligible for meta-analysis. Our analysis revealed that there is an increased risk (OR=1.01; 95% CI:1.00 - 1.02) of MI with a 10 μg/m 3 increase in PM 10 after a lag 0 and lag 1 delay. Conclusions Our findings indicate that PM 10 exposure is associated with an increased risk of MI. This can aid in informing environmental policy-making, personal-level preventative measures, and global public health action.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".