刘芳盈, 姜雯珈, 杜相学, 丛斌, 李平, 翟慎永. 2012年—2014年某市呼吸系统疾病死亡的空间分析[J]. 环境卫生学杂志, 2017, 7(3): 188-191, 196. DOI: 10.13421/j.cnki.hjwsxzz.2017.03.002
    引用本文: 刘芳盈, 姜雯珈, 杜相学, 丛斌, 李平, 翟慎永. 2012年—2014年某市呼吸系统疾病死亡的空间分析[J]. 环境卫生学杂志, 2017, 7(3): 188-191, 196. DOI: 10.13421/j.cnki.hjwsxzz.2017.03.002
    LIU Fangying, JIANG Wenjia, DU Xiangxue, CONG Bin, LI Ping, ZHAI Shenyong. Spatial Analysis of Respiratory Disease Mortality in a City in 2012—2014[J]. Journal of Environmental Hygiene, 2017, 7(3): 188-191, 196. DOI: 10.13421/j.cnki.hjwsxzz.2017.03.002
    Citation: LIU Fangying, JIANG Wenjia, DU Xiangxue, CONG Bin, LI Ping, ZHAI Shenyong. Spatial Analysis of Respiratory Disease Mortality in a City in 2012—2014[J]. Journal of Environmental Hygiene, 2017, 7(3): 188-191, 196. DOI: 10.13421/j.cnki.hjwsxzz.2017.03.002

    2012年—2014年某市呼吸系统疾病死亡的空间分析

    Spatial Analysis of Respiratory Disease Mortality in a City in 2012—2014

    • 摘要:
      目的 分析某市2012年—2014年呼吸系统疾病死亡的区域空间分布,为呼吸系统疾病的健康监测和重点干预提供技术依据。
      方法 根据《中国疾病预防控制系统—死因登记报告信息系统》2012年—2014年某市88个乡镇的呼吸系统疾病死亡数据,基于GIS进行空间分析。
      结果 Moran LISA分析表明,2012年、2013年、2014年和2012年—2014年呼吸系统疾病死亡率均存在具有统计学意义的高低聚集(P < 0.05),高-高分布区域主要为高青县A镇、B街道办和C镇,沂源县D镇、E镇和F镇;Getis-Ord Gi*分析显示,呼吸系统疾病死亡的热点区域主要聚集在高青县A镇、B街道办和C镇,沂源县D镇、E镇、F镇、N街道办、P镇、Q镇、R镇(P < 0.05);Kriging空间分布图显示,各年度呼吸系统疾病死亡趋势基本一致,死亡率较高区域为高青县西部、沂源县南部、临淄区东部区域;空间分析结果与该市社会经济特征的区域分布存在一致性,人口城市化水平低、第一产业比重较大和医疗条件较差的区域,呼吸系统疾病死亡率较高。
      结论 某市呼吸系统疾病死亡存在具有统计学意义的高低聚集,高青县西部和沂源县南部为高风险区域。

       

      Abstract:
      Objective To analyze regional spatial distribution of respiratory disease in a city in 2012—2014, and to provide technical basis for respiratory health monitoring and priority intervention.
      Method According to 88 towns' data of respiratory disease death from the National Cancer Registration of China in a city from 2012 to 2014, the spatial analysis was carried on the GIS-based software.
      Results Moran LISA analysis showed that there were statistically significant aggregations on the mortality of respiratory disease in 2012, 2013, 2014 and 2012—2014(P < 0.05). High-High pattern areas were town A, Street B, town C of Gaoqing and town D, town E, town F of Yiyuan. Getis-Ord Gi* analysis demonstrated hot spots regions of respiratory disease deaths mainly gathered in town A, Street B, town C of Gaoqing and town D, town E, town F, Street N, town P, town Q and town R of Yiyuan.(P < 0.05). Kriging spatial distribution chart showed the same trend of respiratory disease deaths each year, with a higher mortality rate in western Gaoqing, southern Yiyuan and eastern Linzi. The spatial analysis results were consistent with the regional distribution of socioeconomic characteristics of the city. The mortality of respiratory disease was higher where the level of urbanization was low with large proportion of primary industry and poor medical conditions.
      Conclusions There were statistically significant aggregations on the mortality of respiratory disease in a city. The high-risk areas of respiratory disease deaths were western Gaoqing and southern Yiyuan.

       

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