您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(医学版)》

山东大学学报(医学版) ›› 2017, Vol. 55 ›› Issue (8): 88-94.doi: 10.6040/j.issn.1671-7554.0.2016.1437

• 公共卫生与管理学 • 上一篇    下一篇

地理加权回归在脑卒中病因探索中的应用

王纪传1,刘瑞红2,李东芝3,薛付忠4   

  1. 1.淄博市第一人民医院感染性疾病科, 山东 淄博 255200;2.山东省地方病防治研究所克山病防治科, 山东 济南 250014;3.沂源县疾病预防控制中心, 山东 淄博 256100;4.山东大学公共卫生学院生物统计学系, 山东 济南 250012
  • 收稿日期:2016-11-04 出版日期:2017-08-10 发布日期:2017-08-10
  • 通讯作者: 薛付忠. E-mail:xuefzh@sdu.edu.cn E-mail:xuefzh@sdu.edu.cn

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

摘要: 目的 探讨脑卒中与其他疾病(心肌梗死、恶性肿瘤、传染病等)的空间依从关系,分析其是否存在共同的地理危险因子。 方法 收集2011~2014年沂源县脑卒中及其他疾病的发病数据、全死因死亡数据及人口学数据,应用地理加权回归(GWR)模型定量分析各因素与脑卒中的空间依从关系。 结果 (1) 除北部个别区域的心肌梗死发病密度与脑卒中发病呈负相关关系外,沂源县的心肌梗死与脑卒中呈正相关协同变化关系,且这种协同变化由西向东逐渐减弱;(2) 恶性肿瘤与脑卒中呈正相关协同变化关系,且这种协同变化关系由北向南逐渐减弱;(3) 除西部个别区域的传染病与脑卒中呈负相关关系外,沂源县传染病与脑卒中呈正相关协同变化关系,这种协同变化关系在中部大于西部和东部,且呈由北向南的递减趋势。 结论 脑卒中与心肌梗死、恶性肿瘤及传染病之间呈现空间协同变化关系,提示可能存在特定的共同地理社会因素。

关键词: 脑卒中, 地理加权回归, 空间异质性, 地理信息系统

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

中图分类号: 

  • R188.2
[1] Feigin VL, Forouzanfar MH, Krishnamurthi R, et al. Global and regional burden of stroke during 1990-2010: findings from the Global Burden of Disease Study 2010[J]. Lancet, 2014, 383(9913): 245-254.
[2] 王陇德. 中国脑卒中防治报告(2015)[M]. 北京:中国协和医科大学出版社, 2015: 67.
[3] Sun HX, Zou XY, Liu LP. Epidemiological factors of stroke: a survey of the current status in China[J]. J Stroke, 2013, 15(2): 109-114.
[4] 国家心血管病中心. 中国心血管病报告2014[M].北京:中国大百科全书出版社, 2015: 6.
[5] Ikehara S, Iso H, Date C, et al. Salt preference and mortality from stroke and coronary heart disease for Japanese men and women: the JACC study[J]. Prev Med, 2012, 54(1): 32-37.
[6] Guasch-Ferre M, Babio N, Martinez-Gonzalez MA, et al. Dietary fat intake and risk of cardiovascular disease and all-cause mortality in a population at high risk of cardiovascular disease[J]. Am J Clin Nutr, 2015, 102(6): 1563-1573.
[7] Fotheringham AS, Charlton M, Brunsdon CF. The geography of parameter space: an investigation into spatial non-stationarity[J]. Int J Geogr Inf Sci, 1996, 10(5): 605-627.
[8] Fotheringham AS, Charlton M, Brunsdon CF. Two techniques for exploring non-stationarity in geographical data[J]. Geographical Systems, 1997, 4(1): 59-82.
[9] Fotheringham AS, Charlton M. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis[J]. Environ Plan A, 1998, 30(11): 1905-1927.
[10] 李骁. 全球人群肾素—血管紧张素—醛固酮通路内关键基因的空间遗传学研究[D]. 济南:山东大学, 2011. LI Xiao. Worldwide spatial genetic structure of key genes in renin-angiotensin-aldosterone system——explore the new evolutionary ecological evidence for thrifty genotype hypothesis[D]. Jinan: Shandong University, 2011.
[11] 张冰冰. 小空间尺度上手足口病时空流行病学研究[D]. 济南:山东大学, 2013. ZHANG Bingbing. Spatial-temporal epidemiology of HFMD on a smaller scale[D]. Jinan:Shandong University, 2013.
[12] Vine MF, Degnan D, Hanchette C. Geographic information systems: their use in environmental epidemiologic research[J]. Environ Health Perspect, 1997, 105(6): 598-605.
[13] Miranda ML, Casper M, Tootoo J, et al. Putting chronic disease on the map: building GIS capacity in state and local health departments[J]. Prev Chronic Dis, 2013, 10: E100. doi: 10.5888/pcd10.120321.
[14] 刘云霞, 刘言训, 张冰冰, 等. 基于GWR模型的结核病空间流行病学研究[J]. 中国防痨杂志, 2013,35(5): 343-346. LIU Yunxia, LIU Yanxun, ZHANG Bingbing, et al. Spatial epidemiology study on tuberculosis based on geographical weighted regression model[J].Chin J Antituberc, 2013, 35(5): 343-346.
[15] 李骁, 薛付忠. 地理权重回归在人类群体空间遗传结构中的应用[J]. 山东大学学报(医学版), 2011,49(2): 119-124. LI Xiao, XUE Fuzhong. Application of the geographically weighted regression model in spatial genetic structure of the human population[J]. Journal of Shandong University(Health Sciences), 2011, 49(2): 119-124.
[1] 李敏,王春霞,夏冰,朱茜,孙苑潆,王淑康,薛付忠,贾红英. 健康管理人群脑卒中风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 93-97.
[2] 曲立新,时兴华,杜怡峰. 急性缺血性脑卒中患者血浆PMP及EMP含量与预后的相关性[J]. 山东大学学报(医学版), 2016, 54(12): 32-36.
[3] 陈海丽, 顾娇阳, 张文静, 袁琳冉, 郑娟, 袁中瑞. 经典Wnt信号通路在大鼠脑缺血后血管新生中的作用[J]. 山东大学学报(医学版), 2015, 53(4): 31-36.
[4] 郑娟, 李政, 张文静, 袁琳冉, 樊书菠, 刘玉刚, 袁中瑞. Caveolin-1对脑缺血大鼠血管新生的影响[J]. 山东大学学报(医学版), 2015, 53(10): 16-20.
[5] 刘结梅, 黄国志, 刘健, 蔡奇芳. 早期康复治疗对脑卒中后肩手综合征及 上肢运动功能的影响[J]. 山东大学学报(医学版), 2014, 52(S2): 59-60.
[6] 蔡毅,龙发青,曾超胜,苏庆杰,吴海荣,吴映曼,李鹏翔,周经霞,王德生,张余辉. 缺血性脑卒中二级预防中高血压防治的现状及其影响因素[J]. 山东大学学报(医学版), 2013, 51(3): 76-79.
[7] 陈瑨1,祁珍华1,江虹1,张灿灿1,江平胤2,党红梅2,赵鹏2,张红静1 . 脑卒中偏瘫患者D型人格特征[J]. 山东大学学报(医学版), 2013, 51(2): 104-.
[8] 李骁,薛付忠. 地理权重回归在人类群体空间遗传结构中的应用[J]. 山东大学学报(医学版), 2011, 49(2): 119-124.
[9] 邢月梅,曹枫林. 心理干预对预防卒中后抑郁发生的疗效观察[J]. 山东大学学报(医学版), 2010, 48(5): 161-162.
[10] 刘云霞 李士雪 王忠东 薛付忠. 基于时空重排扫描统计量的结核病聚集性研究[J]. 山东大学学报(医学版), 2009, 47(12): 122-125.
[11] 成玉,薛付忠,季晓康,胡平,邵琦,张桂琴 . 基于地理信息系统的中国人群HLA-A、B位点的空间遗传结构[J]. 山东大学学报(医学版), 2008, 46(5): 542-546.
[12] 邵琦,薛付忠,王洁贞,成玉,张桂琴. 人类群体遗传空间结构异质性变异函数模型[J]. 山东大学学报(医学版), 2008, 46(2): 111-114.
[13] 唐芳,康殿民,邢顺来,王洁贞,王志强,胡平,张玉军 . 疾病空间分布的“等值线-面积”多重分形模型及其应用[J]. 山东大学学报(医学版), 2006, 44(11): 1154-1158.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!