Air Pollution Exposure and COVID-19 Outcomes: A Systematic Review and Meta-analysis of Short- and Long-term Effects
Bibliographic record
Abstract
Our objective was to determine the relationship between short-term and long-term exposure to air pollution and COVID-19 mortality and morbidity through a systematic review and meta-analysis. To do so, Scopus, PubMed, and Web of Science databases were searched for original studies up to February 1, 2023. Observational studies reporting risk estimates for the association between air pollution exposure and COVID-19 outcomes were included. The methodological quality of the selected articles was assessed using the Newcastle-Ottawa scale (NOS). Pooled estimates were calculated using a random effects model. We employed the I² statistic and chi-square test to assess heterogeneity among studies. Egger’s and Begg’s tests were used to evaluate potential publication bias. A total of 2823 articles were identified in the initial database search. After screening, 12 studies met the inclusion criteria and were included in the meta-analysis. The results indicated a significant association between PM10 and O3 exposure and COVID-19 mortality, with relative risks of 1.02 (95% confidence interval: 1.01 to 1.04) and 1.09 (95% confidence interval: 1.04 to 1.14), respectively. Our meta-analysis suggests that exposure to O3 and PM10 is associated with an increased risk of COVID-19 mortality. Additionally, the study found a significant link between exposure to NO2 , PM2.5, and PM10 and increased COVID-19 mortality, with PM2.5 showing the strongest association. These findings underscore the need for effective policies to mitigate the health impacts of air pollution and highlight the importance of integrated strategies to address the broader effects of climate change.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".