JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES) ›› 2017, Vol. 55 ›› Issue (8): 88-94.doi: 10.6040/j.issn.1671-7554.0.2016.1437

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Application of the geographical weighted regression model to explore the cause of stroke

WANG Jichuan1, LIU Ruihong2, LI Dongzhi3, XUE Fuzhong4   

  1. 1. Department of Infectious Diseases, the First Peoples Hospital of Zibo, Zibo 255200, Shandong, China;
    2. Department of Keshan Disease, Shandong Institute for Endemic Disease Control, Jinan 250014, Shandong, China;
    3. Yiyuan County Center for Disease Control and Prevention, Zibo 256100, Shandong, China;
    4. Department of Biostatistics, School of Public Health, Shandong University, Jinan 250012, Shandong, China
  • Received:2016-11-04 Online:2017-08-10 Published:2017-08-10

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Abstract: Objective To explore the spatial relationship between stroke and other diseases(myocardial infarction, malignant tumor, and infectious disease, et al)and investigate the common geographical risk factor among them. Methods The data of pathogenesis, all-cause mortality and demography of the patients with stroke and other diseases in Yiyuan County were collected from 2011 to 2014. Geographical weighted regression(GWR)model was constructed to analyze the spatial correlation between stroke and other disease. Results (1) The incidence density of myocardial infarction was positively associated with stroke, except in the northern region, and the coefficients were gradually weakened from west to east. (2) The incidence density of cancer was positively associated with stroke and the coefficients were gradually weakened from north to south. (3) The incidence density of infectious diseases was positively associated with stroke, except in the west region, and the coefficients were greater in central region than in west or east, and in central region, the coefficients were gradually weakened from north to south. Conclusion The spatial correlation between stroke and myocardial infarction, cancer and infectious diseases suggests that certain geographical and social factors may exist.

Key words: Geographical weighted regression, Geographic information system, Stroke, Spatial heterogeneity

CLC Number: 

  • R188.2
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