Impact of a long‐term air pollution exposure on the case fatality rate of COVID‐19 patients—A multicity study
Bibliographic record
Abstract
Abstract Background There is preliminary evidence of the long-term exposure to air pollution will affect the outcome of patients with COVID-19. More information is needed about relationship between long-term exposure to air pollution and case fatality rate (CFR) of patients with COVID-19.Methods In this study, we have collected the data of Air Quality Index (AQI), PM2.5, PM10, SO2, NO2 and O3 from 14 representative cities in China in the past 5 years, and calculated the case fatality rate of COVID-19 in the corresponding city. First, we explored correlation relationship between CFR and long-term air quality indicators. Then, we try to point out the air pollutants that affect the level of CFR and evaluated their predictive value.Results We have observed a positive correlation between the CFR and AQI (1-year, 3-year, 5-year), PM2.5 (1-year, 3-year, 5-year), and PM10 (1-year, 3-year, 5-year). Meanwhile, AQI (3-year, 5-year) and PM2.5 (1-year, 3-year, 5-year) were significantly higher in the high CFR group. Moderate predictive value of air pollution indicator to CFR such as AQI (1-year, 3-year, 5-year), PM2.5 (1-year, 3-year, 5-year) have been found.Conclusions Our results indicate that long-term exposure to the environment with severe air pollution is associated with CFR of COVID-19. Air pollutants such as PM2.5 may have potential ability to predict the CFR of COVID-19.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".