Visualizing and forecasting the association of air quality and health outcomes in Ontario, Canada
Bibliographic record
Abstract
Research has shown that air pollution is associated with risks of development and worsening of chronic diseases. The Air Quality Health Index (AQHI) is a numerical scale that reports air quality and health risk, and includes messages that advise on health risk reduction actions according to AQHI levels. Our study aimed to (1) characterize geographical variations between air pollution (AQHI) and health outcomes (incidence, prevalence, and health services use) of asthma, COPD, diabetes, and hypertension; (2) forecast the trend of associations using observed data; and (3) develop visualization tools that help the public identify risks of air pollution and health outcomes. Observed trends of AQHI and health outcomes from 2003 to 2014 were plotted and forecasted up to 2025, while maps showed their geographical variations. Overall, the highest incidence and prevalence of each disease were observed in regions with higher annual mean AQHI. Chronic diseases' acute health services use was higher in northern Ontario, while physician office visits were higher in southern Ontario. The positive correlations between AQHI and health outcomes in Ontario suggests that increasing public awareness of potential health risks of air pollution is important, especially among people with chronic diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".