Background: We aimed to analyze the yet unclear correlation between air pollutant concentrations (AP) and out-of-hospital cardiac arrest (OHCA) in Shenzhen, China.
Methods: A 5-year time series analysis of all OHCA events reported to the Shenzhen Emergency Center was conducted. Quasi-Poisson regression, controlling for meteorological variables (daily mean relative temperature and humidity) with multivariable fractional polynomial and using Fourier series to adjust for long-term trends and account for periodic patterns, was used to assess the association among particulate matter of 2.5 μm (PM2.5), ozone (O3), particulate matter of ≥10 μm (PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and OHCA.
Results: Data from 16,769 patients who experienced OHCA were analyzed. An increase of 10 μg/m3 in PM2.5 was associated with a higher risk of OHCA (relative risk (RR): 1.026 [95% confidence interval [CI]: 1.001-1.053]) on lag day 1. A similar increase in PM10 was linked to an immediate risk of OHCA on the onset day (RR: 1.02 [95% CI: 1.005-1.036]) and a cumulative risk on lag day 1 (RR: 1.021 [95% CI: 1.003-1.039]). An increased risk of OHCA was associated with NO2 and O3 exposure, while a reduced risk of OHCA was associated with SO2 and CO exposure in the subsequent 5 days. The relationship between PM2.5 and OHCA varied by gender and arrest rhythm. A reduction in the average daily PM2.5 concentration by 1 µg/m³ could decrease the incidence of OHCA attributable to PM2.5 exposure by 4.60%, while a reduction by 3 µg/m³ could decrease it by 18.41% on lag day 1. PM2.5 was significantly associated with the occurrence of OHCA on lag day 1. This association was modulated by gender and arrest rhythm.
Conclusion: Improving the levels of PM2.5, NO2, and O3 could decrease the risk of OHCA and the demand for emergency medical service related to PM2.5 exposure.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1093/ehjacc/zuaf013 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!