Spatial Autocorrelation of Disease Prevalence in South Korea Using 2012 Community Health Survey Data

Abstract

As a basic research to investigate geographical variations of diseases, this study analyzes and compares spatial patterns of 24 different diseases in South Korea using prevalence rate data provided by Community Health Survey in 2012. Descriptive statistical analysis, global Moran's I computation, and disease mapping were conducted to examine spatial associations and patterns of each disease. After the unique spatial patterns and distinctive spatial associations of each disease were observed, we concluded that 12 diseases displayed statistically significant spatial autocorrelation while the other 12 showed no spatial associations. This study suggests that diseases are caused by different risk factors and possess different etiological mechanisms. Furthermore, the study may lay foundation for future studies of geographical variations of disease prevalence in South Korea. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Spatiotemporal analyses of the epidemiological characteristics of diabetes mellitus

“…Kim et al [ 7 ] confirmed that the prevalence of DM varied by region using stepwise regression analysis and decision trees. Other researchers also found that the prevalence of DM was significantly higher in rural areas and regions with a high proportion of the elderly population in Korea [ 8 ]. These results are similar to those of other studies abroad that analyzed the relationships of the degree of aging and socioeconomic level with DM at the regional level.…”

Analysis of Spatial Distribution of Hypertension Prevalence and Its Related Factors based on the Model of Social Determinants of Health

“…The variables of individual-level and regional-level factors affecting hypertension prevalence in the regions (si/ gun/gu) were selected based on previous studies such as Lee [21], Kim and Kang [28], and Ewing [15].…”

“…As described above, it has been found that the prevalence rate of chronic diseases is different according to the spatial specificity of the region, and there are differences depending on the regional variables [20,28]. For these reasons, there is a need for research to identify regional-level factors related to hypertension prevalence through spatial analysis that reflects the spatial dependence, which is a unique geographical feature of space.…”

“…However, no prior studies have carried out a multi-level analysis to stratify and estimate the characteristics of individual-level factors and regional-level factors [5]. In addition, there is still a lack of studies which has visualized the regional distribution of prevalence of hypertension, has represented the differences in the prevalence due to the spatial specificity of the region, and has identified and analyzed regional variables [20,28].…”

A Closer Look at the Bivariate Association between Ambient Air Pollution and Allergic Diseases: The Role of Spatial Analysis

“…Although levels of air pollutants including SO 2 , NO 2 , and PM 10 in major cities have not considerably increased in the last 10 years [ 31 ], air pollution in Seoul, particularly PM 10 , has increased more compared to other large cities such as Paris, Los Angeles, and London [ 32 , 33 ]. A small number of studies on allergic diseases conducted in Korea have taken into account spatial autocorrelation in ambient air pollutants [ 34 , 35 ], while others focus upon the prevalence of allergic diseases [ 36 ]. However, no studies simultaneously account for spatial autocorrelation in their bivariate analysis.…”