Associations of outdoor fine particulate air pollution and cardiovascular disease: Results from the Prospective Urban and Rural Epidemiology Study in China (PURE-China)
Bibliographic record
Abstract
Evidence on whether an excess risk of incidence and mortality of cardiovascular disease (CVD) among people exposed to a high level of ambient PM2.5 in low- and middle-income countries (LMICs) is lacking. This study aimed to investigate the associations between long-term exposure to ambient fine particulate matter<2.5 µm (PM2.5) concentrations and the risk of incidence and mortality of CVD in a large cohort study from 115 communities. In this cohort study, we followed 42 160 adults aged 35–75 years at baseline who enrolled in the Prospective Urban and Rural Epidemiology Study conducted in China (PURE-China) between 2005 and 2009 with ambient PM2.5 estimates, and followed up until August 2021. Cox proportional hazards frailty models were used to estimate the associations between long-term mean outdoor PM2.5 concentrations and CVD events, CVD mortality, and all-cause mortality. During a median follow-up period of 11.8 years, we documented 2 190 deaths, including 732 CVD deaths. There were 4 559 (10.8 %) of 42 160 participants who experienced incident total CVD, among them there were 861 myocardial infarctions (MI) and 2 338 S. The 3-year median concentration of ambient PM2.5 before the cohort commencement was 52.7 µg/m3 (interquartile range [IQR] 30.3–74.6). In full adjusted model, a 10 µg/m3 increase in PM2.5 was associated with a hazard ratio (HR) of 1.12 (95 % CI 1.11–1.14) for major CVD and 1.03 (95 % CI 1.01–1.05) for all-cause mortality. Besides, long-term PM2.5 concentrations had a significantly positive gradient association with total CVD and a similar pattern of associations with other CVD outcomes was observed. This study demonstrated that long-term ambient PM2.5 concentrations is positively associated with increased risks of CVD in adults aged 35–70 years from China. This finding reinforces the need for policymakers to adopt more effective strategies to improve air quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".