Communicating air pollution-related health risks to the public: An application of the Air Quality Health Index in Shanghai, China
Bibliographic record
Abstract
The Air Quality Health Index (AQHI) was originally developed in Canada. However, little is known about its validity in communicating morbidity risks. We aimed to establish the AQHI in Shanghai, China, and to compare the associations of AQHI and existing Air Pollution Index (API) with daily mortality and morbidity. We constructed the AQHI as the sum of excess total mortality associated with individual air pollutants, and then adjusted it to an arbitrary scale (0-10), according to a time-series analysis of air pollution and mortality in Shanghai from 2001 to 2008. We examined the associations of AQHI with daily mortality and morbidity, and compared these associations with API from 2005 to 2008. The coefficients of short-term associations of total mortality with particulate matter with an aerodynamic diameter less than 10 μm (PM(10)), PM(2.5) and nitrogen dioxide (NO(2)) were used in the establishment of AQHI. During 2005-2008, the AQHI showed linear non-threshold positive associations with daily mortality and morbidity. A unit increase of the PM(10)-AQHI was associated with a 0.90% [95% (confidence interval, CI), 0.43 to 1.37], 1.04% (95%CI, 0.04 to 2.04), 1.62% (95%CI, 0.39 to 2.85) and 0.51% (95%CI, 0.09 to 0.93) increase of current-day total mortality, hospital admissions, outpatient visits and emergency room visits, respectively. The PM(2.5)-AQHI showed quite similar effect estimates with the PM(10)-AQHI. In contrast, the associations for API were much weaker and generally statistically insignificant. The AQHI, compared with the existing API, provided a more effective tool to communicate the air pollution-related health risks to the public.
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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 | 0.004 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".