Impacts from air pollution on respiratory disease outcomes: a meta-analysis
Bibliographic record
Abstract
Introduction: Air pollution is widely acknowledged as a significant factor in respiratory outcomes, including coughing, wheezing, emergency department (ED) visits, and even death. Although several literature reviews have confirmed the association between air pollution and respiratory outcomes, they often did not standardize associations across different studies and overlooked other increasingly impactful pollutants such as trace metals. Recognizing the importance of consistent comparison and emissions of non-exhaust particles from road traffic, this study aims to comprehensively evaluate the standardized effects of various criteria pollutants and trace metals on respiratory health. Methods: We conducted a comprehensive meta-analysis of peer-reviewed journal articles on air pollution and respiratory outcomes published between 1 January 2000, and 1 June 2024. The study included children (age < 18 years), adults (age ≥ 18 years), and all age groups exposed to criteria pollutants established by the US Environmental Protection Agency National Ambient Air Quality Standards and over 10 trace metals. Using databases, such as PubMed, MEDLINE, Web of Science Core Collection, and Google Scholar, we identified 579 relevant articles. After rigorous screening and quality assessment using the Newcastle-Ottawa Scale, 50 high-quality studies were included. We converted various reported outcomes (e.g., odds ratios, relative risk, and percent increase) to a standardized odds ratio (OR) for comparability and performed meta-analyses using R 4.4.0 and related packages, ensuring the robustness of our findings. Results: ) and other pollutants, with children showing higher susceptibility than adults. The analysis of trace metals also showed significant associations; however, these findings require cautious interpretation due to the small number of studies. Conclusion: Our study supports associations between air pollutants, including non-exhaust trace metals, and respiratory outcomes across different age groups. The findings underscore the need for stringent environmental health policies and further research, especially in regions with higher pollution levels. The future studies should consider long-term and short-term exposures separately and include diverse populations to improve the accuracy and generalizability of the results.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.084 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".