Using medications to prevent or reduce the impact of poor air quality on health: a systematic review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».