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

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  • Zhensheng Wang

    (School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Qingyun Du

    (School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Key Laboratory of GIS, Ministry of Education, 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
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • De-nan Lin

    (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)

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.

Suggested Citation

  • Zhensheng Wang & Qingyun Du & Shi Liang & Ke Nie & De-nan Lin & Yan Chen & Jia-jia Li, 2014. "Analysis of the Spatial Variation of Hospitalization Admissions for Hypertension Disease in Shenzhen, China," IJERPH, MDPI, vol. 11(1), pages 1-21, January.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:1:p:713-733:d:31880
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    References listed on IDEAS

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    Cited by:

    1. Zhensheng Wang & Ke Nie, 2019. "Measuring Spatial Patterns of Health Care Facilities and Their Relationships with Hypertension Inpatients in a Network-Constrained Urban System," IJERPH, MDPI, vol. 16(17), pages 1-22, September.
    2. Qingyun Du & Mingxiao Zhang & Yayan Li & Hui Luan & Shi Liang & Fu Ren, 2016. "Spatial Patterns of Ischemic Heart Disease in Shenzhen, China: A Bayesian Multi-Disease Modelling Approach to Inform Health Planning Policies," IJERPH, MDPI, vol. 13(4), pages 1-14, April.
    3. Yuliang Xi & Fu Ren & Shi Liang & Jinghua Zhang & De-Nan Lin, 2014. "Spatial Analysis of the Distribution, Risk Factors and Access to Medical Resources of Patients with Hepatitis B in Shenzhen, China," IJERPH, MDPI, vol. 11(11), pages 1-23, November.
    4. Yanxia Wang & Qingyun Du & Fu Ren & Shi Liang & De-nan Lin & Qin Tian & Yan Chen & Jia-jia Li, 2014. "Spatio-Temporal Variation and Prediction of Ischemic Heart Disease Hospitalizations in Shenzhen, China," IJERPH, MDPI, vol. 11(5), pages 1-26, May.

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