Long-Term Exposure to Outdoor Air Pollution and COVID-19 Mortality: an ecological analysis in England
Bibliographic record
Abstract
Abstract There is an urgent need to examine what individual and environmental risk factors are associated with COVID-19 mortality. This objective of this study is to investigate the association between long term exposure to air pollution and COVID-19 mortality. We conducted a nationwide, ecological study using zero-inflated negative binomial models to estimate the association between long term (2014-2018) small area level exposure to NO x , PM 2.5 , PM 10 and SO 2 and COVID-19 mortality rates in England adjusting for socioeconomic factors and infection exposure. We found that all four pollutant concentrations were positively associated with COVID-19 mortality. The increase in mortality risk ratio per inter quarter range increase was for PM 2.5 :11%, 95%CIs 6%-17%), PM 10 (5%; 95%CIs 1%-11%), NOx (11%, 95%CIs 6%-15%) and SO 2 (7%, 95%CIs 3%-11%) were respectively in adjusted models. Public health intervention may need to protect people who are in highly polluted areas from COVID-19 infections.
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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) | 0.002 | 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 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".