Distributed Lag Analyses of Daily Hospital Admissions and Source-Apportioned Fine Particle Air Pollution
Bibliographic record
Abstract
BACKGROUND: Past time-series studies of the health effects of fine particulate matter [aerodynamic diameter ≤ 2.5 µm (PM2.5)] have used chemically nonspecific PM2.5 mass. However, PM2.5 is known to vary in chemical composition with source, and health impacts may vary accordingly. OBJECTIVE: We tested the association between source-specific daily PM2.5 mass and hospital admissions in a time-series investigation that considered both single-lag and distributed-lag models. METHODS: Daily PM2.5 speciation measurements collected in midtown Manhattan were analyzed via positive matrix factorization source apportionment. Daily and distributed-lag generalized linear models of Medicare respiratory and cardiovascular hospital admissions during 2001-2002 considered PM2.5 mass and PM2.5 from five sources: transported sulfate, residual oil, traffic, steel metal works, and soil. RESULTS: Source-related PM2.5 (specifically steel and traffic) was significantly associated with hospital admissions but not with total PM2.5 mass. Steel metal works-related PM2.5 was associated with respiratory admissions for multiple-lag days, especially during the cleanup efforts at the World Trade Center. Traffic-related PM2.5 was consistently associated with same-day cardiovascular admissions across disease-specific subcategories. PM2.5 constituents associated with each source (e.g., elemental carbon with traffic) were likewise associated with admissions in a consistent manner. Mean effects of distributed-lag models were significantly greater than were maximum single-day effect models for both steel- and traffic-related PM2.5. CONCLUSIONS: Past analyses that have considered only PM2.5 mass or only maximum single-day lag effects have likely underestimated PM2.5 health effects by not considering source-specific and distributed-lag effects. Differing lag structures and disease specificity observed for steel-related versus traffic-related PM2.5 raise the possibility of distinct mechanistic pathways of health effects for particles of differing chemical composition.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".