Analysis of the Spatial Variation of Hospitalization Admissions for Hypertension Disease in Shenzhen, China

Zhensheng Wang, Qingyun Du, Shi Liang, Ke Nie, Lin De-nan, Yan Chen and Jia-jia Li
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Zhensheng Wang: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Qingyun Du: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Shi Liang: Shenzhen Center for Health Information, Renmin Road North 2210, Luohu District, Shenzhen 518001, China
Ke Nie: School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Lin De-nan: Shenzhen Center for Health Information, Renmin Road North 2210, Luohu District, Shenzhen 518001, China
Yan Chen: Shenzhen Center for Health Information, Renmin Road North 2210, Luohu District, Shenzhen 518001, China
Jia-jia Li: Shenzhen Center for Health Information, Renmin Road North 2210, Luohu District, Shenzhen 518001, China

IJERPH, 2014, vol. 11, issue 1, 1-21

Abstract: In China, awareness about hypertension, the treatment rate and the control rate are low compared to developed countries, even though China’s aging population has grown, especially in those areas with a high degree of urbanization. However, limited epidemiological studies have attempted to describe the spatial variation of the geo-referenced data on hypertension disease over an urban area of China. In this study, we applied hierarchical Bayesian models to explore the spatial heterogeneity of the relative risk for hypertension admissions throughout Shenzhen in 2011. The final model specification includes an intercept and spatial components (structured and unstructured). Although the road density could be used as a covariate in modeling, it is an indirect factor on the relative risk. In addition, spatial scan statistics and spatial analysis were utilized to identify the spatial pattern and to map the clusters. The results showed that the relative risk for hospital admission for hypertension has high-value clusters in the south and southeastern Shenzhen. This study aimed to identify some specific regions with high relative risk, and this information is useful for the health administrators. Further research should address more-detailed data collection and an explanation of the spatial patterns.

Keywords: hypertension; Hierarchical Bayesian models; spatial scan statistics; analysis scale; Shenzhen; urban China (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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