The establishment of National Air Quality Health Index in China
Bibliographic record
Abstract
BACKGROUND: A new Air Quality Health Index (AQHI) was developed in Canada or several single cities as a promising health risk communication tool. OBJECTIVES: To construct a national AQHI in China and compare its validity in predicting daily mortality risk with the existing Air Quality Index (AQI). METHODS: We established the AQHI as the sum of excess total mortality risks associated with multiple air pollutants in 272 representative Chinese cities from 2013 to 2015 (termed as "total AQHI"). The mortality risks per unit change of air pollutant concentrations were determined according to a time-series analysis in each city. Separate AQHIs were established for subgroups classified by age and sex and for main cardiopulmonary diseases (termed as "specific AQHIs"). For validation, AQHIs and AQI were established using the data of 2015 (N = 272) and compared their associations with daily mortality using the data of 2013-2014 (N = 144). RESULTS: The concentration-response coefficients of fine particulate matter, nitrogen dioxide and ozone were adopted in constructing AQHI. There were almost linear exposure-response relationships between AQHIs and daily mortality. The total AQHI and specific AQHIs had very similar associations with daily mortality. AQHI and AQI showed similar associations with daily cause-specific mortality in terms of average magnitude, numbers of cities of positive associations and model fit statistics. CONCLUSIONS: AQHI may have comparable performance with AQI in communicating acute health risks of air pollution in China. There seems no need to establish specific AQHIs for different age groups, gender and causes of deaths.
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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) | 0.002 | 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 teacher head, 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".