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 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 | 0.003 | 0.004 |
| 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) | 0.001 | 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 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".