Effects of PM2.5 exposure and air temperature on risk of cardiovascular disease: evidence from a prospective cohort study
Bibliographic record
Abstract
Background and aims Evidence from extensive cohort studies about the individual and combined associations of air pollution and air temperature with cardiovascular disease (CVD) morbidity is limited. This study aimed to examine the long-term effects of PM 2.5 exposure and air temperature on CVD based on a cohort study of middle-aged and older populations in China. Methods A total of 9,316 non-CVD adults (≥40 years old) who joined the China Health and Retirement Longitudinal Study between 2011 and 2018 were included in our analysis. The two-year average PM 2.5 concentration and air temperature of the city where participants lived were calculated. The Cox proportional hazards model was conducted to analyze the associations of PM 2.5 exposure and air temperature with CVD morbidity. Results In the multivariable-adjusted model, each 10 μg/m 3 rise in 2-y PM 2.5 concentration was associated with an increased risk of developing CVD (hazard ratio [HR]: 1.30; 95% confidence interval [CI]: 1.27–1.32). Compared with individuals in the bearable temperature group, those with low temperatures had a higher risk of CVD (HR: 1.77; 95% CI: 1.53–2.04). Stratified analyses found that cardiovascular metabolic risk factors could not change these associations. Compared with individuals in the low-level PM 2.5 exposure and bearable temperature group, those in the high-level PM 2.5 exposure and low-temperature group had a 7.08 times higher risk of CVD (95% CI: 5.55–9.03). Conclusion Long-term PM 2.5 exposure and low air temperature are associated with a higher risk of CVD. Consequently, efforts to reduce air pollution and enhance protection against cold temperatures are vital for mitigating CVD risk.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".