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Impact of weather and pollution on the rate of cerebrovascular events in a large metropolitan area

医学 置信区间 大都市区 二氧化氮 泊松回归 空气污染 氮氧化物 臭氧 微粒 环境科学 氮氧化物 大气科学 气象学 环境卫生 内科学 地理 生态学 人口 化学 有机化学 病理 地质学 工程类 生物 燃烧 废物管理
作者
Francesco Versaci,Sabrina Anticoli,Francesca Romana Pezzella,Marilena MANGIARDI,Alessandro Di Giosa,Giada Marchegiani,Simone Calcagno,Riccardo Di Pietro,Giacomo Frati,Sebastiano Sciarretta,Armando Perrotta,Mariangela Peruzzi,Elena Cavarretta,Leonardo Roever,Barbara Antonazzo,Stefano RONZONI,Benedetta VERSACI,Giuseppe Biondi‐Zoccai
出处
期刊:Panminerva Medica [Edizioni Minerva Medica]
卷期号:64 (1) 被引量:5
标识
DOI:10.23736/s0031-0808.21.04525-0
摘要

Despite mounting evidence, there is uncertainty on the impact of the interplay between weather and pollution features on the risk of acute cerebrovascular events (CVE). We aimed at appraising role of weather and pollution on the daily risk of CVE.Anonymized data from a hub CVE center in a large metropolitan area were collected and analyzed according to weather (temperature, pressure, humidity, and rainfall) and pollution (carbon monoxide [CO], nitrogen dioxide [NO2], nitrogen oxides [NOX], ozone [O3], and particulate matter [PM]) on the same and the preceding days. Poisson regression and time series analyses were used to appraise the association between environmental features and daily CVE, distinguishing also several subtypes of events.We included a total of 2534 days, with 1363 days having ≥1 CVE, from 2012 to 2017. Average daily rate was 1.56 (95% confidence interval: 1.49; 1.63) for CVE, with other event rates ranging between 1.42 for stroke and 0.01 for ruptured intracranial aneurysm. Significant associations were found between CVE and temperature, pressure, CO, NO2, NOX, O3, and PM <10 µm (all P<0.05), whereas less stringent associations were found for humidity, rainfall, and PM <2.5 µm. Time series analysis exploring lag suggested that associations were stronger at same-day analysis (lag 0), but even environmental features predating several days or weeks were significantly associated with events. Multivariable analysis suggested that CO (point estimate 1.362 [1.011; 1.836], P=0.042) and NO2 (1.011 [1.005; 1.016], P<0.001) were the strongest independent predictors of CVE.Environmental features are significantly associated with CVE, even several days before the actual event. Levels of CO and NO2 can be potentially leveraged for population-level interventions to reduce the burden of CVE.

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