Road proximity, air pollution, noise, green space and neurologic disease incidence: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Emerging evidence links road proximity and air pollution with cognitive impairment. Joint effects of noise and greenness have not been evaluated. We investigated associations between road proximity and exposures to air pollution, and joint effects of noise and greenness, on non-Alzheimer's dementia, Parkinson's and Alzheimer's disease and multiple sclerosis within a population-based cohort. METHODS: We assembled administrative health database cohorts of 45-84 year old residents (N ~ 678,000) of Metro Vancouver, Canada. Cox proportional hazards models were built to assess associations between exposures and non-Alzheimer's dementia and Parkinson's disease. Given reduced case numbers, associations with Alzheimer's disease and multiple sclerosis were evaluated in nested case-control analyses by conditional logistic regression. RESULTS: Road proximity was associated with all outcomes (e.g. non-Alzheimer's dementia hazard ratio: 1.14, [95% confidence interval: 1.07-1.20], for living < 50 m from a major road or < 150 m from a highway). Air pollutants were associated with incidence of Parkinson's disease and non-Alzheimer's dementia (e.g. Parkinson's disease hazard ratios of 1.09 [1.02-1.16], 1.03 [0.97-1.08], 1.12 [1.05-1.20] per interquartile increase in fine particulate matter, Black Carbon, and nitrogen dioxide) but not Alzheimer's disease or multiple sclerosis. Noise was not associated with any outcomes while associations with greenness suggested protective effects for Parkinson's disease and non-Alzheimer's dementia. CONCLUSIONS: Road proximity was associated with incidence of non-Alzheimer's dementia, Parkinson's disease, Alzheimer's disease and multiple sclerosis. This association may be partially mediated by air pollution, whereas noise exposure did not affect associations. There was some evidence of protective effects of greenness.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".