On the lagged non-linear association between air pollution and COVID-19 cases in Belgium

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-02-01 DOI:10.1016/j.sste.2024.100709

Sara Rutten , Marina Espinasse , Elisa Duarte , Thomas Neyens , Christel Faes

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引用次数: 0

Abstract

Exposure to air pollution has been proposed as a determinant of COVID-19 dynamics. While the connection between air pollution and COVID-19 has been established for several countries worldwide, few such analyses exist in Belgium. Therefore, we examine this potential association in Belgium, using COVID-19 cases of all 581 municipalities between September 2020 and January 2022. We employ a Bayesian spatio-temporal negative binomial model, allowing for potential non-linear and lagged effects of pollution. Comparing different single-pollutant models, we find that the model providing the best fit to the data contains black carbon. At the median pollution level, a cumulative risk of

1.66 (1.57, 1.74)

over 8 weeks is found for this pollutant, compared to the 5% pollution quantile. In addition, the study reveals a remarkable similarity in COVID-19 incidence between adjacent municipalities in Belgium.

Our findings suggest paying careful attention to highly air polluted areas when preparing for future pandemics of respiratory diseases.

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关于比利时空气污染与COVID-19病例之间的滞后非线性关联

暴露于空气污染已被认为是COVID-19动态的决定因素。虽然世界上几个国家已经建立了空气污染与COVID-19之间的联系，但比利时很少有这样的分析。因此，我们利用2020年9月至2022年1月期间比利时所有581个城市的COVID-19病例，研究了这种潜在关联。我们采用贝叶斯时空负二项模型，考虑污染的潜在非线性和滞后效应。比较不同的单一污染物模型，我们发现含有黑碳的模型对数据的拟合效果最好。在中位数污染水平上，与5%污染分位数相比，该污染物在8周内的累积风险为1.66（1.57,1.74）。此外，该研究还揭示了比利时相邻城市之间COVID-19发病率的惊人相似性。我们的研究结果表明，在为未来的呼吸系统疾病大流行做准备时，要特别关注空气污染严重的地区。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Spatial and Spatio-Temporal Epidemiology PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

5.10

自引率

8.80%

发文量