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Spatial Patterns of Ischemic Heart Disease in Shenzhen, China: A Bayesian Multi-Disease Modelling Approach to Inform Health Planning Policies

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  • Qingyun Du

    (School of Resources 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
    Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Mingxiao Zhang

    (School of Resources and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Yayan Li

    (School of Resources and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Hui Luan

    (School of Planning, Faculty of Environment, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada)

  • Shi Liang

    (Shenzhen Prevention and Treatment Center for Occupational Diseases, Guiyuan Street North 70, Luohu District, Shenzhen 518001, China)

  • Fu Ren

    (School of Resources 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
    Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

Abstract

Incorporating the information of hypertension, this paper applies Bayesian multi-disease analysis to model the spatial patterns of Ischemic Heart Disease (IHD) risks. Patterns of harmful alcohol intake (HAI) and overweight/obesity are also modelled as they are common risk factors contributing to both IHD and hypertension. The hospitalization data of IHD and hypertension in 2012 were analyzed with three Bayesian multi-disease models at the sub-district level of Shenzhen. Results revealed that the IHD high-risk cluster shifted slightly north-eastward compared with the IHD Standardized Hospitalization Ratio (SHR). Spatial variations of overweight/obesity and HAI were found to contribute most to the IHD patterns. Identified patterns of IHD risk would benefit IHD integrated prevention. Spatial patterns of overweight/obesity and HAI could supplement the current disease surveillance system by providing information about small-area level risk factors, and thus benefit integrated prevention of related chronic diseases. Middle southern Shenzhen, where high risk of IHD, overweight/obesity, and HAI are present, should be prioritized for interventions, including alcohol control, innovative healthy diet toolkit distribution, insurance system revision, and community-based chronic disease intervention. Related health resource planning is also suggested to focus on these areas first.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:4:p:436-:d:68545
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    References listed on IDEAS

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    1. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    2. 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.
    3. 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.
    4. Leonhard Knorr‐Held & Nicola G. Best, 2001. "A shared component model for detecting joint and selective clustering of two diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 73-85.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

    1. Zirong Ye & Li Xu & Zi Zhou & Yafei Wu & Ya Fang, 2018. "Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China," IJERPH, MDPI, vol. 15(1), pages 1-18, January.

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