Long Term Exposure to Black Carbon and Cardiovascular Mortality: A Study based on A Dynamic Three-Dimensional Exposure Model in an Elderly Cohort
Bibliographic record
Abstract
Background/Aim: It has been well documented that air pollution is linked to adverse cardiovascular and respiratory health effects. Nevertheless, there has only been a few epidemiologic studies focusing on health effects of long term exposure of black carbon (BC). In addition, most of the studies have not accounted for the 3 Dimensional landscapes in cities and mobility of the population. We aim to estimate BC based on a novel dynamic 3 dimensional (D3D) land use regression (LUR) model and assess the long-term effects on mortality in a large elderly cohort in Hong Kong. Methods: We conducted a cohort study of BC and cardiovascular mortality among 66,820 subjects aged 65 years old or older in Hong Kong from 1998-2011. BC concentrations were estimated by LUR model and assigned to all participants based on their residential addresses at baseline periods, adjusted the vertical and dynamic components. Cox regression models were used to estimate the hazard ratios (HRs) of mortality associated with BC. Results: Statistically associations were observed not only for all natural causes mortality (HR=1.05; 95%CI: 1.03, 1.07) and cardiovascular disease (HR=1.1; 95CI%: 1.04, 1.17) but also for the two subcategories, IHD ( HR=1.11; 95%CI: 1.04, 1.17) and cerebrovascular disease ( HR=1.07; 95%CI: 1.01, 1.15) per IQR increase of BC. Conclusions: This cohort study demonstrated that long-term exposure to ambient BC was associated with an increased risk of cardiovascular mortality. These findings suggested that BC may play a role in the association between traffic related air pollutants and mortality.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".