Spatial associations of daily PM2.5 concentration with cardiovascular and pulmonary morbidity in Korea

Abstract

Air pollution, particularly fine particulate matter less than 2.5 μm in diameter (PM2.5), contributes to respiratory and cardiovascular diseases and poses significant public health risks worldwide. This study evaluated the short-term effects of PM2.5 on hospital admissions for cardiovascular and respiratory diseases, with additional analyses to identify vulnerable populations based on regional characteristics. The present study analyzed data from 249 Korean communities between 2006 and 2021. Data on daily hospital admissions for cardiovascular and respiratory diseases were obtained from the National Health Insurance Service. Data on PM2.5 concentrations were sourced from air quality modeling. Additional data on regional characteristics, including the regional deprivation index, proportion of elderly residents, education levels, and greenness levels, were also collected. We used case time series analysis to assess the associations between PM2.5 concentrations and hospital admissions for cardiovascular and respiratory diseases and explored effect modification by regional characteristics with stratified analyses. The mean numbers of daily cardiovascular admissions and respiratory admissions were 5.68 ± 5.46 and 6.46 ± 8.03, respectively. The mean PM2.5 concentrations were 23.58 ± 13.66 μg/m3. A10 μg/m³ increment in daily PM2.5 concentration was associated with increase of cardiovascular and respiratory hospitalization by 0.94% (95% CI: 0.84%, 1.04%) and 1.43% (95% CI: 1.34%, 1.52%), respectively. Regional characteristics analysis showed significant disparities, with higher risks for hospital admissions in areas with lower deprivation and low greenness. This study highlights the significant short-term health impacts of PM2.5 on respiratory and cardiovascular hospital admissions in Korean communities. The findings underscore the critical role of regional and demographic factors in modulating these effects, identifying socio-economic areas, age structure of the population, lower education levels, and low greenness as key vulnerability factors.