Road, rail, and air transportation noise in residential and workplace neighborhoods and blood pressure (RECORD Study)
Bibliographic record
Abstract
Associations between road traffic noise and hypertension have been repeatedly documented, whereas associations with rail or total road, rail, and air (RRA) traffic noise have rarely been investigated. Moreover, most studies of noise in the environment have only taken into account the residential neighborhood. Finally, few studies have taken into account individual/neighborhood confounders in the relationship between noise and hypertension. We performed adjusted multilevel regression analyses using data from the 7,290 participants of the RECORD Study to investigate the associations of outdoor road, rail, air, and RRA traffic noise estimated at the place of residence, at the workplace, and in the neighborhoods around the residence and workplace with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension. Associations were documented between higher outdoor RRA and road traffic noise estimated at the workplace and a higher SBP [+1.36 mm of mercury, 95% confidence interval (CI): +0.12, +2.60 for 65-80 dB(A) vs 30-45 dB(A)] and DBP [+1.07 (95% CI: +0.28, +1.86)], after adjustment for individual/neighborhood confounders. These associations remained after adjustment for risk factors of hypertension. Associations were documented neither with rail traffic noise nor for hypertension. Associations between transportation noise at the workplace and blood pressure (BP) may be attributable to the higher levels of road traffic noise at the workplace than at the residence. To better understand why only noise estimated at the workplace was associated with BP, our future work will combine Global Positioning System (GPS) tracking, assessment of noise levels with sensors, and ambulatory monitoring of BP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".