Particulate matter air pollution and COVID-19 infection, severity, and mortality: A systematic review
Bibliographic record
Abstract
Abstract Background and objective Ecological studies indicate ambient particulate matter ≤2.5mm (PM 2.5 ) air pollution is associated with poorer COVID-19 outcomes. However, these studies cannot account for individual heterogeneity and often have imprecise estimates of PM 2.5 exposure. We review evidence from studies using individual-level data to determine whether PM 2.5 increases risk of COVID-19 infection, severe disease, and death. Methods Systematic review of case-control and cohort studies, searching Medline, Embase, and WHO COVID-19 up to 30 June 2022. Study quality was evaluated using the Newcastle-Ottawa Scale. Results were pooled with a random effects meta-analysis, with Egger’s regression, funnel plots, and leave-one-out and trim-and-fill analyses to adjust for publication bias. Results N =18 studies met inclusion criteria. A 10μg/m3 increase in PM 2.5 exposure was associated with 66% (95% CI: 1.31-2.11) greater odds of COVID-19 infection (N=7) and 127% (95% CI: 1.41-3.66) increase in severe illness (hospitalisation or worse) (N=6). Pooled mortality results (N=5) were positive but non-significant (OR 1.40; 0.94 to 2.10). Most studies were rated “good” quality (14/18 studies), though there were numerous methodological issues; few used individual-level data to adjust for confounders like socioeconomic status (4/18 studies), instead using area-based indicators (12/18 studies) or not adjusting for it (3/18 studies). Most severity (9/10 studies) and mortality studies (5/6 studies) were based on people already diagnosed COVID-19, potentially introducing collider bias. Conclusion There is strong evidence that ambient PM 2.5 increases the risk of COVID-19 infection, and weaker evidence of increases in severe disease and mortality. Funding This review was completed as a Scholarly Intensive Placement project by NS, which received no funding. Competing interests The authors declare no competing interests. Registration This study was registered on PROSPERO on 8 July 2022 (CRD42022345129): https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022345129
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".