Within- and between-city contrasts in nitrogen dioxide and mortality in 10 Canadian cities; a subset of the Canadian Census Health and Environment Cohort (CanCHEC)
Bibliographic record
Abstract
The independent and joint effects of within- and between-city contrasts in air pollution on mortality have been investigated rarely. To examine the differential effects of between- versus within-city contrasts in pollution exposure, we used both ambient measurements and land use regression models to assess associations with mortality and exposure to nitrogen dioxide (NO2) among ~735,600 adults in 10 of the largest Canadian cities. We estimated exposure contrasts partitioned into within- and between-city contrasts, and the sum of these as overall exposures, for every year from 1984 to 2006. Residential histories allowed us to follow subjects annually during the study period. We calculated hazard ratios (HRs) adjusted for many personal and contextual variables. In fully-adjusted, random-effects models, we found positive associations between overall NO2 exposures and mortality from non-accidental causes (HR per 5 p.p.b.: 1.05; 95% confidence interval (CI): 1.03-1.07), cardiovascular disease (HR per 5 p.p.b.: 1.04; 95% CI: 1.01-1.06), ischaemic heart disease (HR per 5 p.p.b.: 1.05; 95% CI: 1.02-1.08) and respiratory disease (HR per 5 p.p.b.: 1.04; 95% CI: 0.99-1.08), but not from cerebrovascular disease (HR per 5 p.p.b.: 1.01; 95% CI: 0.96-1.06). We found that most of these associations were determined by within-city contrasts, as opposed to by between-city contrasts in NO2. Our results suggest that variation in NO2 concentrations within a city may represent a more toxic mixture of pollution than variation between cities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".