Long term exposure to PM<sub>2.5</sub> chemical components associated with prevalence of cardiovascular diseases in China
Bibliographic record
Abstract
Introduction Ambient fine particulate matter pollution (PM2.5) has been widely associated with cardiovascular disease (CVD). However, less is known about the contribution of different chemical components of PM2.5 to CVD using a nationally representative sample in China. Methods A nationally representative sample of older adults was recruited from 31 provinces, municipalities, or autonomous regions of China by the fourth national Urban and Rural Elderly Population Survey in 2015. We estimated the annual average concentrations of PM2.5 and its five dust-free chemical components (black carbon [BC], organic matter [OM], sulphate [ $ {{\text{SO}_{\text{4}}^{\text{2-}} }}$ ], nitrate [ ${ {\text{N}\text{O}}_{\text 3}^{\text -} }$ ], and ammonium [ $ { {\text{N}\text{H}}_{\text 4}^{\text +} }$ ]) at geocoded residential addresses with the spatial resolution of 10×10 km using bilinear interpolation. Logistic regression models were constructed to estimate the associations between PM2.5 chemical components and prevalence of self-reported CVD, and potential reducible fractions were further estimated using counterfactual analyses. Results A total of 220,425 participants with a mean age of 69.73 years, 52.24% females, and 6.08% minor ethnicity were included in the study, of which 55,837 (25.3%) reported having CVD. An interquartile range (IQR) increment in annual PM2.5 chemical components was associated with significantly elevated risk of CVD prevalence. The odds ratios were 1.254 (95% CI: 1.235-1.275, IQR: 7.11 µg/m3) for $ {\text{N}\text{O}}_{\text3}^{\text-} $ , 1.197 (95% CI: 1.178-1.216, IQR: 4.35 µg/m3) for $ {\text{N}\text{H}}_{\text 4}^{\text +} $ , 1.187 (95% CI: 1.173-1.202, IQR: 5.34 µg/m3) for OM, 1.122 (95% CI: 1.107-1.137, IQR: 0.97 µg/m3) for BC, and 1.106 (95% CI: 1.089-1.123, IQR: 4.67 µg/m3) for $ {\text{S}\text{O}}_{\text 4}^{\text 2-} $ . The associations were significantly stronger in those older than 70 years. Conclusions Our study suggests that long-term exposure to PM2.5 chemical components could increase the risk of CVD prevalence. Future air pollution guidelines target reducing specific PM2.5 chemical components may help alleviate the burden of CVD.
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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 | 0.001 | 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 | 0.001 | 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) | 0.002 | 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".