Effects of Ambient Particulate Matter on Mortality in European and North American Cities: An Analysis Within the Aphena Project
Bibliographic record
Abstract
SS1-05 Introduction: APHENA is a collaborative effort joining the multicity European APHEA and the U.S. NMMAPS projects as well as independent Canadian investigators. The effects of ambient particulate matter (PM10) were studied in 90 U.S. cities (16 with daily measurements and 74 with one of 6 day measurements) with a population from 250,000 to 9 million; 22 European cities (with daily measurements), population from 200,000 to 7 million; and 12 Canadian cities (with one of 6 day measurements), population from 200,000 to 20 million. The daily number of total, respiratory, and cardiovascular deaths was studied for all ages and among those above and under 75 years of age. Methods: The analysis was done in 2 stages. The first stage concerns city-specific datasets and involves extensive exploratory and sensitivity analyses using Poisson regression models. To control for several time-varying confounders, we used both natural and penalized splines as smoothers. We also applied models using varying degrees of freedom for seasonality control. The second stage involves combining the effect estimates obtained from the first stage and attempting to explain heterogeneity by predefined potential effect modifiers. Results: Results for all studied outcomes and from models using varying methods for seasonality control are presented. Thus, for example, the daily increase of cardiovascular deaths associated with 10-μg/m3 increase in PM10 concentrations using 8 degrees of freedom per year and natural splines, in cities with daily measurements, was 0.31% (95% confidence interval: 0.04 to 0.59) for European cities and 0.30% (−0.18 to 0.78) for U.S. cities. The effects estimated for Canadian cities were generally higher. Conclusions: In summary, we found an increase in mortality (in most cases statistically significant) with increasing PM10 concentrations. Although the estimates in Europe and the United States were similar, in Canada, they were considerably higher. The effects were higher in the elderly compared with those under 75 years of age. Higher effects were observed in cardiovascular mortality. Effect modification patterns between the 2 continents showed differences with environmental and climatic variables but consistency with socioeconomic status indicators.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".