Daily Mortality/Morbidity and Air Quality: Using Multivariate Time Series with Seasonally Varying Covariances
Bibliographic record
Abstract
Abstract We study the associations between daily mortality and short-term variations in the ambient concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in four cities in Canada. First, a novel multivariate time series model within Bayesian framework is proposed for exposure assessment, where the response is a mixture of Gamma and Half-Cauchy distributions and the correlations between pollutants vary seasonally. A case-crossover design and conditional logistic regression model is used to relate exposure to disease data for each city, which then are combined to obtain a global estimate of exposure health effects allowing exposure uncertainty. The results suggest that every 10 ppb increase in O3 is associated with a 3.88% (95% credible interval [CI], 2.5%, 5.18%) increase in all-cause mortality, a 5.04% (2.84%, 7.43%) increase in circulatory mortality, a 7.87% (2.4%, 12.9%) increase in respiratory mortality, a 0.76% (0.19%, 1.35%) increase in all-cause morbidity and a 6.6% (0.58%, 12.7%) increase in respiratory morbidity. Similarly, every 10 ppb increase in NO2 is associated with a 2.13% (0.42%, 3.87%) increase in circulatory morbidity. The health impacts of PM2.5 are not found to be present once other pollutants are accounted for.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".