Environmental inequality and circulatory disease mortality gradients
Bibliographic record
Abstract
STUDY OBJECTIVE: Studies in Europe and North America have reported that living in a disadvantaged neighbourhood is associated with an increased incidence of coronary heart disease. The aim of this study was to test the hypotheses that exposure to traffic and air pollution might account for some of the socioeconomic differences in mortality rates in a city where residents are covered by universal health insurance. DESIGN: Cohort mortality study. Individual postal codes used to derive: (1) socioeconomic status from census data; (2) mean air pollution levels from interpolation between governmental monitoring stations; (3) proximity to traffic from the geographical information system. Analysis conducted with Cox proportional hazards models. SETTING: Hamilton Census Metropolitan Area, Ontario, Canada, on the western tip of Lake Ontario (population about 480,000). PARTICIPANTS: 5228 people, aged 40 years or more, identified from register of lung function laboratory at an academic respirology clinic between 1985 and 1999. MAIN RESULTS: Circulatory disease (cardiovascular and stroke) mortality rates were related to measures of neighbourhood deprivation. Circulatory disease mortality rates were also associated with indices of long term ambient pollution at the subjects' residences (relative risk 1.06, 1.00 to 1.13) and with proximity to traffic (relative risk 1.40, 1.08 to 1.81). Subjects in more deprived neighbourhoods had greater exposure to ambient particulate and gaseous pollutants and to traffic. CONCLUSIONS: At least some of the observed social gradients in circulatory mortality arise from inequalities in environmental exposure to background and traffic air pollutants.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".