Using medications to prevent or reduce the impact of poor air quality on health: a systematic review
Bibliographic record
Abstract
Abstract Introduction Particulate matter (PM) is a mixture of tiny solid materials and liquid particles in the air that can trigger inflammatory reactions in multiple body systems, including the respiratory, cardiovascular and endocrine systems. Currently, there are no licensed pharmacological interventions to prevent or modify the effects of PM on different organs. However, several existing medications have shown promising results towards modifying or preventing the negative impact of PM. Aim To conduct a systematic review to explore pharmacological interventions that could potentially prevent, delay or treat the effects of PM on human health. Methods This systematic review complied with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and was registered with PROSPERO database (CRD42023476448). Four databases were searched including; MEDLINE, Embase, PsycINFO and Scopus. Keywords were arranged into different relevant sets such as ‘air pollution’, ‘medication’ and ‘prevention’/’treatment’. All the resulting titles, abstracts and full-texts were screened independently by two researchers. Only peer reviewed articles published in English were included. A tailored data extraction sheet was used to collate all relevant data, including the study description (i.e. country, year), study design (i.e. prospective), medication information (i.e. type of medication, dose, indication), population (i.e. demographics and disease), effect on air pollution (i.e. treatment or prevention). Quality assessment was conducted using Newcastle Ottawa tool. Ethical approval was not required to undertake this systematic review. Results The search produced 689 articles, 676 of which were removed at the title (n=463), and abstract (n=184) and full-text (n=29) stages. Thirteen articles were included, 10 of which were rated ‘good’ quality, two ‘fair’ quality, and one ‘poor’ quality. There was a range of pharmacological interventions evaluated, including beta blockers, oral anti-diabetic agents, statins, non-steroidal anti-inflammatory drugs (NSAIDs), systematic glucocorticoids, inhalers (adrenergic, glucocorticoid and anticholinergic inhalers) and theophylline. All studies focused only on PM, with six providing information on the particle diameter (e.g., PM10, PM2.5 and PM0.1). Statins, NSAIDs and bronchodilators demonstrated the most significant impact on reducing the damage caused by PM on both the cardiovascular and respiratory systems through anti-inflammatory, vascular re-modelling, and broncho-dilating effect. These medications had already been prescribed in these study patients for other indications (e.g., diabetes or asthma). None of the included studies used a medication for the prevention or treatment of air pollution effects as an indication. Conclusion Some medications significantly prevented/treated the effects of poor air quality; however, this was inconsistent across studies. However, all studies were conducted in high to middle-high income countries; findings may have been different in low-income countries. More research is required on the effective medication dose, duration of administration, if being prescribed solely for air pollution protection, and benefits and risks for more vulnerable populations (i.e. multimorbid). References 1. Anderson JO, Thundiyil JG, Stolbach A. Clearing the air: a review of the effects of particulate matter air pollution on human health. Journal of medical toxicology. 2012 Jun;8:166-75. 2. Arias-Pérez RD, Taborda NA, Gómez DM, Narvaez JF, Porras J, Hernandez JC. Inflammatory effects of particulate matter air pollution. Environmental Science and Pollution Research. 2020 Dec;27(34):42390-404.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".