Effects of ambient air pollution on incident Parkinson’s disease in Ontario, 2001 to 2013: a population-based cohort study
Bibliographic record
Abstract
Background: Despite recent studies linking air pollution to neurodegenerative illness, evidence relating air pollution and Parkinson's disease (PD) remains scarce. We conducted a population-based cohort study in Ontario, Canada, to determine the association between air pollution and incident PD. Methods: Using health administrative databases, we identified all adults aged 55-85 years, free of PD, and who lived in Ontario on 1 April 2001 (∼2.2 million). Individuals were followed up until 31 March 2013. We derived long-term average exposures to fine particulate matter (particles ≤2.5 µm in diameter, or PM2.5), nitrogen dioxide (NO2) and ozone from satellite-based estimates, land-use regression models and optimal interpolation methods, respectively. Using 2-year lags in exposures, we linked these estimates to individuals' annual postal codes from 1994 (7 years before cohort inception). We applied spatial random-effects Cox proportional hazards models, adjusting for individual- and area-level characteristics. We also performed sensitivity analyses, such as considering longer lags in exposures and stratifying by selected characteristics. Results: During the study period, we identified 38 745 newly diagnosed cases of PD. Each interquartile increment (3.8 µg/m3) of PM2.5 was associated with a 4% increase in incident PD (95% confidence interval, 1.01-1.08) after adjusting for various covariates. We also found positive associations for NO2 and ozone [hazard ratios (HRs) ranged from 1.03 to 1.04]. The associations for all exposures were unaltered with various sensitivity analyses except for considering longer lags, which somewhat attenuated the estimates, particularly for NO2 and ozone. Conclusions: Exposure to air pollution, especially PM2.5, was found to be related to incident PD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".