Association between Multiple Environmental Factors with Neurological Diseases in Metro Vancouver, British Columbia, Canada
Bibliographic record
Abstract
Background: Limited evidence suggests the potential for associations between air pollution with neurodegenerative disorders such as Multiple Sclerosis (MS), Parkinson’s Disease (PD) and dementia. Further, other environmental factors such as noise and green spaces have yet to be examined.Objectives: We aim to assess the effects of spatially co-varying environmental exposures - noise, nitrogen dioxide (NO2), fine particulate matter(PM2.5) and green space - on the incidence of MS, PD and dementia in the Vancouver, British Columbia (BC), Canada metropolitan region. Here we present initial analyses for MS.Methods: Cases and age- and sex-matched controls were identified in a population-based (N = 674,176) cohort of adults derived from health insurance records. Incident cases from 1999 to 2002 were identified by relevant diagnostic codes and pharmaceutical dispensation data. Environmental exposures were assigned to each study subject based on their residential postal codes, accounting for changes in residence. We applied conditional logistic regression to estimate the increased odds of developing MS, PD and dementia in relation to an interquartile range increase in exposures, while adjusting for neighborhood level median income level.Results: Compared to their matched controls, MS cases (N = 289) were increased by 21% (95% CI: 0.79, 1.86) in relation to noise exposure, and by 11% (95% CI: 0.79, 1.55) and 7% (95% CI: 0.76, 1.52) for NO2 and PM2.5 respectively. A change in neighborhood green space was associated with a 38% decrease in the odds of a MS case (OR= 0.62, 95% CI: 0.38, 0.98).Conclusions: Noise and air pollution exposure may increase the risk of MS incidence while neighborhood green space had a protective effect.
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 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 | 0.000 | 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) | 0.003 | 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".