上海交通大学学报(医学版)

• 论著(卫生事业管理) • 上一篇    下一篇

应用地理信息系统分析河南省HIV感染者的空间分布及影响因素

刘 露,陈 于,王 帅,冯 为,黄文捷,韩令力,李 阳   

  1. 重庆医科大学 公共卫生与管理学院劳动与环境卫生学教研室, 重庆 400016
  • 出版日期:2014-02-28 发布日期:2014-03-25
  • 通讯作者: 陈 于, 电子信箱: lucychenyu2000@yahoo.com.cn。
  • 作者简介:刘 露(1988—), 女, 硕士生; 电子信箱: liulu4567@sina.cn。

Application of geographic information system in spatial distribution of HIV infected people in Henan province

LIU Lu, CHEN Yu, WANG Shuai, FENG Wei, HUANG Wen-jie, HAN Ling-li, LI Yang   

  1. Department of Occupational and Environmental Health, School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China
  • Online:2014-02-28 Published:2014-03-25

摘要:

目的 分析河南省HIV感染者的空间分布及其影响因素,为河南省HIV防治提供科学依据。方法 收集2010—2011年河南省17个县(市)HIV感染者资料,利用ArcGIS 10.0建立数据库,采用GeoDa_0.9.5.i软件进行空间聚集性分析和空间回归分析。结果 河南省HIV感染者2010年和2011年呈现空间聚集性,Moran′s I值分别为0.283 0和0.283 1,且均具有统计学意义(P<0.05);在空间回归模型上,人均医疗机构数对河南省2010年和2011年的HIV感染率均有影响(Z2010=-0.014 8, P2010=0.046 6; Z2011=-0.015 2,P2011=0.009 2)。结论 河南省HIV感染者的空间分布为非随机分布,存在明显的聚集性,且人均医疗机构数在空间层面上影响着河南省HIV的感染情况。

关键词: HIV, 空间自相关, 空间回归模型, 人均医疗机构数

Abstract:

Objective To analyze the spatial distributions and influence factors of HIV infected people of Henan province so as to provide scientific evidences for the prevention and treatment of HIV. Methods Based on the HIV data of 17 counties (cities) in Henan province from 2010 to 2011, the spatial database was set up by ArcGIS10.0 and the spatial analysis and spatial regressive analysis were performed by the GeoDa_0.9.5.I software. Results HIV infected people in Henan province in 2010 and 2011 showed spatial aggregation, Moran′s I values were 0.283 0 and 0.283 1 and were statistically significant (P<0.05). The result of spatial regressive analysis showed that the HIV infection rate in Henan province was related to per capita number of medical institutions (Z2010=-0.014 8, P2010=0.046 6; Z2011=-0.015 2, P2011=0.009 2). Conclusion The spatial distribution of HIV infections is non-random and exists significant clustering in Henan province from 2010 to 2011. The HIV infection rate is affected by the per capita number of medical institutions at the spatial level.

Key words: HIV, spatial autocorrelation, spatial autoregressive model, per capita number of medical institutions