The effect of particulate matter size on cardiovascular health in Taipei Basin, Taiwan. (December 2016)

• Record Type: Journal Article
• Authors: Yang, Hsuan-Chia; Chang, Shu-Hao; Lu, Richard; Liou, Der-Ming
• Is Part Of: Computer methods and programs in biomedicine. Volume 137(2016)
• Page Start: 261
• Page End: 268
• Publication Date: 2016-12
• Subjects: Particulate matter -- Cardiovascular disease -- Distributed lag non-linear model; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.08.014
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Distributed Lag Non-linear Model (DLNM) was used to explore the risk effect of different particulate matter sizes such as PM10, PM2.5-10, and PM2.5 . Only PM2.5 is significantly positively correlated with the number of daily outpatient visits with cardiovascular disease. To quantify the effect of different PM sizes on CVD and could be the basis of a local, more detailed environmental study. Abstract: Background: Although the overall effect of particulate matter (PM) on cardiovascular disease (CVD) has been previously documented, the effect of different PM sizes (PM10, PM2.5-10 and PM2.5 ) has not been well studied. This study estimates the effect of different PM sizes on the incidence of CVD in Taipei, Taiwan. Methods: We collected outpatients with CVD from 2006 to 2010 and data on the concentrations of air pollutants such as PM10, PM2.5-10, PM2.5, sulfur dioxide, carbon monoxide, nitrogen dioxide, and ozone. A Distributed Lag Non-linear Model (DLNM) was used to explore the effect of different PM sizes on CVD risk. Results: In high air pollution events, PM2.5 was significantly associated with elevated risk (4.9%) [95% confidence interval (CI): 1.010–1.089] for CVD with increasing interquartile range (IQR) in single air pollutant model. PM2.5-10 and PM10 did not show a significant positive association with CVD in this study. After adjusting for other air pollutants such as SO2, CO, NO2, and O3, the estimated effect of PM2.5 only decreased 0.2%. Moreover, patients under 40 years old did not show a significant association between PM2.5 and CVD. Conclusion: This study demonstrates that only PM2.5 is significantly positively correlated with the number of daily outpatient visits for CVD during high air pollution events.