Bibliographic record
Abstract
The risk of incident hypertension associated with long-term exposure to fine particulate matter (PM<sub>2.5</sub>) was still unclear by studies conducted in North America and Europe, and this relationship has rarely been quantified at higher ambient concentrations typically found in developing countries. We aimed to investigate the association between PM<sub>2.5</sub> and incident hypertension using the large-scale prospective cohorts in China. We included 59 456 participants without hypertension aged ≥18 years from the China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) project. Data on ambient PM<sub>2.5</sub> at participants' residential address were obtained during 2004 to 2015 using a satellite-based spatial-temporal model. Hazard ratios and 95% CIs were calculated for incident hypertension using stratified Cox proportional hazards models with adjustment of potential confounders. The findings indicated that average PM<sub>2.5</sub> concentration from 2004 to 2015 at study participants' address was 77.7 μg/m<sup>3</sup>. During the follow-up of 364 947 person-years, we identified 13 981 incident hypertension cases. Compared with the lowest quartile exposure of PM<sub>2.5</sub>, participants in the highest quartile had an increased risk of incident hypertension with a hazard ratio (95% CI) of 1.77 (1.56-2.00). Each 10 μg/m<sup>3</sup> increment of PM<sub>2.5</sub> concentration could increase 11% risk of hypertension (hazard ratio, 1.11; 95% CI, 1.05-1.17). This cohort study provided the first evidence from China that long-term exposure to PM<sub>2.5</sub> was independently associated with incident hypertension at relatively high ambient concentrations. Stringent strategies on PM<sub>2.5</sub> pollution control are warranted to improve the air quality and contribute to the reduction of disease burden of hypertension in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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 teacher head, 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".