• Francesco Versaci, Sabrina Anticoli, Francesca R 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, and Giuseppe Biondi-Zoccai.
• Unit of UTIC, Hemodynamics and Cardiology, Santa Maria Goretti Hospital, Latina, Italy.
• Panminerva Med. 2022 Mar 1; 64 (1): 17-23.

BackgroundDespite 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.MethodsAnonymized 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.ResultsWe 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.ConclusionsEnvironmental 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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