The association between air pollution and COVID-19 related mortality in Santiago, Chile: A daily time series analysis
Bibliographic record
Abstract
Exposure to ambient air pollution is a risk factor for morbidity and mortality from lung and heart disease. Does short term exposure to ambient air pollution influence COVID-19 related mortality? Using time series analyses we tested the association between daily changes in air pollution measured by stationary monitors in and around Santiago, Chile and deaths from laboratory confirmed or suspected COVID-19 between March 16 and August 31, 2020. Results were adjusted for temporal trends, temperature and humidity, and stratified by age and sex. There were 10,069 COVID-19 related deaths of which 7659 were laboratory confirmed. Using distributed lags, the cumulative relative risk (RR) (95% CI) of mortality for an interquartile range (IQR) increase in CO, NO2 and PM2.5 were 1.061 (1.033–1.089), 1.067 (1.023–1.103) and 1.058 (1.034–1.082), respectively There were no significant differences in RR by sex.. In those at least 85 years old, an IQR increase in NO2 was associated with a 12.7% (95% CI 4.2–22.2) increase in daily mortality. This study provides evidence that daily increases in air pollution increase the risk of dying from COVID-19, especially in the elderly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".