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

{"title":"On the lagged non-linear association between air pollution and COVID-19 cases in Belgium","authors":"Sara Rutten , Marina Espinasse , Elisa Duarte , Thomas Neyens , Christel Faes","doi":"10.1016/j.sste.2024.100709","DOIUrl":null,"url":null,"abstract":"<div><div>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 <span><math><mrow><mn>1</mn><mo>.</mo><mn>66</mn><mspace></mspace><mrow><mo>(</mo><mn>1</mn><mo>.</mo><mn>57</mn><mo>,</mo><mn>1</mn><mo>.</mo><mn>74</mn><mo>)</mo></mrow></mrow></math></span> 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.</div><div>Our findings suggest paying careful attention to highly air polluted areas when preparing for future pandemics of respiratory diseases.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"52 ","pages":"Article 100709"},"PeriodicalIF":2.1000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spatial and Spatio-Temporal Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877584524000765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}

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.

查看原文