Air Health Trend Indicator: Association between Short-Term Exposure to Ground Ozone and Circulatory Hospitalizations in Canada for 17 Years, 1996-2012
Bibliographic record
Abstract
Background: The Air Health Trend Indicator is part of the Canadian Environmental Sustainability Indicators program in Canada. It is designed to estimate public health risk related to short-term exposure to air pollution and to detect trends in the annual national health risks.Design: Daily ozone, hospitalization (three circulatory diseases) and weather data for 24 urban cities (about 54% of Canadians) over a period of 17 years (1996-2012) were used. Through a Bayesian hierarchical model an estimator using 7-year blocks was employed to find trends in annual national associations by season, lag of effect, sex and two age groups (≤65 vs >65).Results: The Canadian population is growing and aging but getting healthier as hospitalization rates have declined from 10% to 8%. Females were hospitalized more than males (57% vs 43%) and circulatory hospitalization was about 16% of all causes. This study examined three circulatory causes: Ischaemic heart disease (IHD, ICD_I20-I25, 40% of cases), other heart disease (OHD, ICD_I30-I52, 31%) and cerebrovascular disease (CEV, ICD_I60-I69, 14%). For warm season the baseline national risk per 10 ppb of 1-day lagged ozone was 0.4% (-0.3%~1.1%) for IHD, 0.7% (0.05%~1.3%) for OHD, and 0.2% (-0.8%~1.2%) for CEV. While an age effect was identified, more difference was detected by sex, with males more vulnerable to ozone for CEV and OHD. While IHD showed decreasing trends for recent years (2009 onwards) regardless age and sex, CEV did slightly increasing trends overall. In contrast, CEV did mixed trends by sex, with an increasing trend for males only.Discussions: The sex-specific differences in circulatory hospitalization risk need further investigations. The study findings could reduce knowledge gap by identifying sub-populations susceptible to ozone by season, age, sex and trend. The temporal trends in ozone’s impact on circulatory hospitalization could help develop air pollution regulations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".