Tracking National and Regional Spatial‐Temporal Mortality Risk Associated with NO<sub>2</sub> Concentrations in Canada: A Bayesian Hierarchical Two‐Level Model
Bibliographic record
Abstract
The association between daily variations in urban air quality and mortality has been well documented using time series statistical methods. This approach assumes a constant association over time. We develop a space-time dynamic model that relaxes this assumption, thus more directly examining the hypothesis that improvements in air quality translate into improvements in public health. We postulate a Bayesian hierarchical two-level model to estimate annual mortality risks at regional and national levels and to track both risk and heterogeneity of risk within and between regions over time. We illustrate our methods using daily nitrogen dioxide concentrations (NO2) and nonaccidental mortality data collected for 1984-2004 in 24 Canadian cities. Estimates of risk and heterogeneity are compared by cause of mortality (cardio-pulmonary [CP] versus non-CP) and season, respectively. Over the entire period, the NO2 risk for CP mortality was slightly lower but with a narrower credible interval than that for non-CP mortality, mainly due to an unusually low risk for a single year (1998). Warm season NO2 risk was higher than cold season risk for both CP and non-CP mortality. For 21 years overall there were no significant differences detected among the four regional NO2 risks. We found overall that there was no strong evidence for time trends in NO2 risk at national or regional levels. However, an increasing linear time trend in the annual between-region heterogeneities was detected, which suggests the differences in risk among the four regions are getting larger, and further studies are necessary to understand the increasing heterogeneity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".