[논문]Spatial analysis of <tex>$PM_{10}$</tex> and cardiovascular mortality in the Seoul metropolitan area

Lim, Yu-Ra; Bae, Hyun-Joo; Lim, Youn-Hee; Yu, Seungdo; Kim, Geun-Bae; Cho, Yong-Sung

doi:10.5620/eht.2014.29.e2014005

Spatial analysis of $PM_{10}$ and cardiovascular mortality in the Seoul metropolitan area 원문보기

Environmental health and toxicology : Eht, v.29, 2014년, pp.5.1 - 5.7

Lim, Yu-Ra (Korea Environment Institute) , Bae, Hyun-Joo (Korea Environment Institute) , Lim, Youn-Hee (Institute of Health and Environment, Seoul National University) , Yu, Seungdo (Environmental Health Research Department, National Institute of Environmental Research) , Kim, Geun-Bae (Environmental Health Research Department, National Institute of Environmental Research) , Cho, Yong-Sung (Environmental Health Research Department, National Institute of Environmental Research)

Abstract ▼ AI-Helper

Objectives Numerous studies have revealed the adverse health effects of acute and chronic exposure to particulate matter less than $10{\mu}m$ in aerodynamic diameter ($PM_{10}$). The aim of the present study was to examine the spatial distribution of $PM_{10}$ concentrations and cardiovascular mortality and to investigate the spatial correlation between $PM_{10}$ and cardiovascular mortality using spatial scan statistic (SaTScan) and a regression model. Methods From 2008 to 2010, the spatial distribution of $PM_{10}$ in the Seoul metropolitan area was examined via kriging. In addition, a group of cardiovascular mortality cases was analyzed using SaTScan-based cluster exploration. Geographically weighted regression (GWR) was applied to investigate the correlation between $PM_{10}$ concentrations and cardiovascular mortality. Results An examination of the regional distribution of the cardiovascular mortality was higher in provincial districts (gu) belonging to Incheon and the northern part of Gyeonggi-do than in other regions. In a comparison of $PM_{10}$ concentrations and mortality cluster (MC) regions, all those belonging to MC 1 and MC 2 were found to belong to particulate matter (PM) 1 and PM 2 with high concentrations of air pollutants. In addition, the GWR showed that $PM_{10}$ has a statistically significant relation to cardiovascular mortality. Conclusions To investigate the relation between air pollution and health impact, spatial analyses can be utilized based on kriging, cluster exploration, and GWR for a more systematic and quantitative analysis. It has been proven that cardiovascular mortality is spatially related to the concentration of $PM_{10}$.

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제안 방법

In this research, various spatial epidemiological analyses were applied to the Seoul metropolitan area between 2008 and 2010 in order to determine the correlation between PM₁₀ and cardiovascular mortality. Kriging was performed on PM₁₀ concentration for to determine spatial distribution, and spatial autocorrelation was determined by using Moran’s I.
In this study, the spatial analyses were used to determine the correlation between PM₁₀ and cardiovascular mortality in the Seoul metropolitan area. To identify the characteristics of spatial distribution with regard to PM₁₀ and cardiovascular mortality, spatial analysis was conducted in each location, and cluster analysis was conducted to examine areas with high a cardiovascular mortality rate.
To identify the characteristics of spatial distribution with regard to PM₁₀ and cardiovascular mortality, spatial analysis was conducted in each location, and cluster analysis was conducted to examine areas with high a cardiovascular mortality rate. Moreover, in order to determine the spatial correlation between PM₁₀ and cardiovascular mortality, spatial distribution of PM₁₀ and cluster areas of cardiovascular mortality were compared, and a spatial correlation was assessed quantitatively using regression analysis.
First, it is an ecological study that uses population data, and it does not integrate personalized data due to the limitations of the data. Therefore, the research does not consider personal characteristics or dynamic factors such as population movement because the analysis is based on population data. Second, cardiovascular mortality can occur due to factors other than air pollution, including environmental factors, socio-economic factors, medical factors, and dietary factors.
Spatial epidemiology involves integrated analysis, such as visualization, exploratory analysis, and modeling based on spatial data regarding environmental exposure and health effects, and it examines the analysis of the spatial correlation and patterns in order to predict and prevent health risk [10,29]. This research utilized distance interpolation, disease mapping, and GWR analysis among various spatial methods in order to identify the correlation between air pollution and health effects, and it produced significant results of the correlation between air pollution and health effects through systematic and quantitative analysis.
To assess the concentration of PM₁₀ in the Seoul metropolitan area between 2008 and 2010, the concentration of PM₁₀ in the 79 provincial districts was calculated using kriging and zonal statistics via ArcGIS 10.1 (ESRI Inc., Redlands, CA, USA).
and cardiovascular mortality in the Seoul metropolitan area. To identify the characteristics of spatial distribution with regard to PM₁₀ and cardiovascular mortality, spatial analysis was conducted in each location, and cluster analysis was conducted to examine areas with high a cardiovascular mortality rate. Moreover, in order to determine the spatial correlation between PM₁₀ and cardiovascular mortality, spatial distribution of PM₁₀ and cluster areas of cardiovascular mortality were compared, and a spatial correlation was assessed quantitatively using regression analysis.

대상 데이터

The study areas were 79 provincial districts in Seoul, Incheon, and Gyeonggi-do within the metropolitan area (25 in Seoul, 11 in Incheon, and 44 in Gyeonggi-do). The metropolitan area is 11,745 km², covering approximately 11.

데이터처리

In this research, a comparison of the PM₁₀ and the hot spot of cardiovascular mortality identified through SaTScan showed that in most cases, the cardiovascular mortality cluster was included within the area with high levels of PM₁₀. Also, a significant correlation was identified as a result of analyzing the PM₁₀ concentration and cardiovascular mortality using the GWR, and the spatial correlation appeared differently in the result of the regional regression coefficient calculation, which indicates a similar result as the previous research results. The reason that the regional spatial correlation between PM₁₀ and cardiovascular mortality appears differently is due to various factors, such as concentration levels and components of the PM₁₀, demographic characteristics, and sensitivity of the population exposed to the PM₁₀, as well as socio-economic factors [27,28].

이론/모형

Cluster detection was performed using software for the spatial and space-time scan statistic (SaTScan) version 9.0 (Martin Kulldorff, Boston, MA, USA) with a Poisson model. Statistical cluster areas of hot spots are mapped based on SaTScan’s likelihood ratio [17].
We obtained daily counts of deaths between January 1, 2008 and December 31, 2010 from the National Statistical Office, South Korea. This study focused on deaths caused by cardiovascular-related diseases (International Classification of Diseases 10th revision [ICD-10], code I00-I99) within the metropolitan area, based on the time of death.
, kriging, which is widely used to estimate the spatial distribution of environmental factors, was applied. To estimate the concentration of PM₁₀ unobserved in this study, ordinary kriging was applied by using the spherical theoretical variogram model as the weight. Compared to the Gaussian model or the exponential model, the calculated error value is minimal in the spherical model [14].

성능/효과

[25] used SaTScan to identify asthma cluster in New York City, and they analyzed the correlation between land use, housing type, and air pollution. The analysis results revealed a quantitative correlation of 69% between asthma cluster and air pollution facilities. Jephcote and Chen [26] utilized the GWR in order to examine the relationship between respiratory hospitalizations of children under the age of 15, socio-economic indicators, and PM₁₀ from automobile emissions in Leicester, UK.
and cardiovascular mortality. The minimum, maximum, and average regression coefficient of PM₁₀ and cardiovascular mortality by region in the GWR were 0.799, 1.562, and 0.956, respectively and a difference in the correlation between PM₁₀ and cardiovascular mortality was reported. The R2 adj of the GWR was 0.
[20] have investigated clusters of mortality cases due to colon cancer, lung cancer, and breast cancer using SaTScan throughout Japan as the subject matter, and the relation between mortality clusters and social clusters (socio-economic indicators and population density) was analyzed. The results of the analysis revealed that the mortality cluster due to lung cancer and breast cancer deaths were the urban areas. Horst and Coco [21] used SaTScan to analyze clusters of patients visiting basic health services and emergency rooms due to respiratory and gastrointestinal diseases.
Jephcote and Chen [26] utilized the GWR in order to examine the relationship between respiratory hospitalizations of children under the age of 15, socio-economic indicators, and PM₁₀ from automobile emissions in Leicester, UK. The results of their study indicated that respiratory hospitalization of children is related to socio-economic indicators and PM₁₀ from automobile emissions, and this relationship differed between regions. In this research, a comparison of the PM₁₀ and the hot spot of cardiovascular mortality identified through SaTScan showed that in most cases, the cardiovascular mortality cluster was included within the area with high levels of PM₁₀.

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