Exposure to ambient particulate matter air pollution, blood pressure and hypertension in children and adolescents: A national cross-sectional study in China
Bibliographic record
Abstract
Air pollution has been associated with elevated blood pressure in adults. However, epidemiological evidence from children and adolescents is limited. We investigated the associations between long-term exposure to particulate matter (PM) air pollution and blood pressure in a large population of children and adolescents. A cross-sectional analysis was performed in a nationally representative sample consisting of 43,745 children and adolescents aged 7 to 18 years in seven provinces in China. Exposure to ambient fine particles (PM2.5) and thoracic particles (PM10) was estimated using spatiotemporal models based on satellite remote sensing, meteorological data and land use information. Mixed-effects (two-level) linear and logistic regression models were used to investigate the associations between PM exposure and systolic blood pressure (SBP), diastolic blood pressure (DBP) and hypertension. After adjustment for a wide range of covariates, every 10 μg/m3 increment in PM2.5 and PM10 concentration was associated with 1.46 [95% confidence interval (CI): 0.05, 2.88] and 1.36 (95% CI: 0.34, 2.39) mmHg increases in SBP, respectively. PM10 was also associated with higher prevalence of hypertension [odds ratio per 10 μg/m3 increment: 1.45 (95% CI: 1.07, 1.95)]. Long-term exposure to ambient PM air pollution was associated with increased blood pressure and higher prevalence of hypertension in children and adolescents. Our findings support air pollution reduction strategies as a prevention measure of childhood hypertension, a well-recognized risk factor of future cardiovascular health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".