APHEIS HEALTH IMPACT ASSESSMENT
Bibliographic record
Abstract
ISEE-90 Introduction: The Apheis programme aims to provide European decision makers, environmental-health professionals and the general public with up-to-date information on air pollution (AP) and public health (PH). For this purpose, Apheis delivers standardised, periodic reports based on health impact assessments (HIAs) in 26 cities in 12 European countries. We describe here the short- and long-term findings of the HIA conducted in Apheis-3. Methods: Apheis developed its own guidelines for data collection and analysis, using the PSAS-9 French programme for attributable-cases calculations and the AirQ-WHO software for years of life lost (YoLL) calculations. Apheis has estimated the acute impact of PM10 and BS on premature mortality using the most recent European exposure-response functions (ERF). For the chronic impact of PM10 and PM2.5 on premature mortality we used Pope’s 2002 ERF. For the purpose of HIA, PM automatic measurements were converted by a local or European correction factor. We performed the present HIA for different scenarios on the health benefits of reducing fine particulate levels. Results: The total population covered in this HIA includes nearly 39 million inhabitants. Black smoke (BS) measurements were provided by 15 cities, Athens showing the highest mean BS levels (77 μg/m3). PM10 measurements were provided by 23 cities, in most of the cities, mean values fall below 50 μg/m3. PM2.5 measurements were provided by 12 cities, mean values are below 20 μg/m3. In the city of Madrid for e.g., in terms of life expectancy, all other things being equal, if annual mean PM2.5 levels (31 μg/m3) would be reduced to 15 μg/m3, the 51 years of life expectancy in a person of 30 years would be increased by 0.2 years, due to reduced risk of death from all causes. Discussion: In addition to the number of attributable cases, Apheis-3 HIA has produced YoLL findings for long-term exposure to fine particulates in Europe. With its monitoring system, Apheis will continue to keep the information we provide as up-to-date and accurate as possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".