Mortality and Hospitalization Linked to Fine Particulate Matter in Canada: Is There a Trend in Risk between 2001 and 2012?
Bibliographic record
Abstract
Background: The Air Health Trend Indicator is designed to estimate public health risk related to short-term exposure to air pollution and to detect trends in annual national health risks. Trends in risk may reflect changes over time in population susceptibility or in composition of fine particulate matter or more generally in the air pollution mix.Design: Daily PM2.5, mortality, morbidity, and weather data for 22 cities (about 50% of the total population) were collected for 2001-2012. A Bayesian hierarchical model was employed to estimate annual national associations by season, lag of effect, sex and age group (≤65 vs >65). Circulatory (ICD_I00-I99) and respiratory (ICD_J00-J99) causes were examined for both mortality and morbidity (hospital admissions). Results: Annual population weighted PM2.5 concentrations decreased gradually by 14% from 8 to7 µg/m3 nationally over the 12 year period. While population has increased by 13%, the ratios of mortality and morbidity to the population have decreased by 6% and 14%, respectively. In relation with PM2.5 both mortality and morbidity showed higher circulatory risk for cold season, higher respiratory risk for warm season, higher risk for seniors (>65), and sex-specific differences. Females were at higher risk (1.9% vs 1.1% per 1 µg/m3) for circulatory mortality but males were at higher risk (0.26% vs 0.02%) for respiratory morbidity. No apparent trends were observed in PM2.5 risk over time.Discussions: Despite downward trends in PM2.5 concentrations, mortality and morbidity rates, there was no apparent trend in PM2.5 risk. The 12-year period may be not long enough to detect trends if they exist. While the age difference in risk has been consistently reported, the sex difference has been inconsistent and thus needs further investigation. These findings could differentiate adverse effects of PM2.5 on mortality and morbidity by identifying sub-populations susceptible to PM2.5 by season, age, and sex.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".