Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State
Bibliographic record
Abstract
BACKGROUND: Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited. OBJECTIVE: We examined the potential association between long-term exposure to particulate matter 2:5 lm in aerodynamic diameter [fine particulate matter (PM 2:5 )] and disease aggravation in Alzheimer's (AD) and Parkinson's (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation. METHODS: We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000(SPARCS -2014) ) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual PM 2:5 concentrations estimated by a prediction model at a 1-km 2 resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y PM 2:5 exposure. We allowed for nonlinear exposure-outcome relationships using penalized splines and accounted for potential confounders. RESULTS: We found a positive nonlinear PM 2:5 -PD association that plateaued above 11 lg=m 3 (RR = 1:09, 95% CI: 1.04, 1.14 for a PM 2:5 increase from 8.1 to 10:4 lg=m 3 ). We also found a linear PM 2:5 -ALS positive association (RR = 1:05, 95% CI: 1.01, 1.09 per 1-lg=m 3 PM 2:5 increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients <70 years of age but found insufficient evidence of effect modification by sex or urbanization level for any of the outcomes. CONCLUSION: Our findings suggest that annual increase in county-level PM 2:5 concentrations may contribute to clinical aggravation of PD and ALS. Importantly, the average annual PM 2:5 concentration in our study was 8:1 lg=m 3 , below the current American national standards, suggesting the standards may not adequately protect the aging population.
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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 | 0.000 | 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) | 0.001 | 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".