地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (4): 502-510.doi: 10.3724/SP.J.1047.2017.00502

• 地理空间分析综合应用 • 上一篇    下一篇

山西省原平市神经管畸形时空分析

陈会宴1,2(), 廖一兰2,*(), 张宁旭2,3, 徐冰2   

  1. 1. 长安大学地球科学与资源学院,西安 710054
    2. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    3. 中国科学院大学,北京 10049
  • 收稿日期:2016-10-13 修回日期:2017-01-16 出版日期:2017-04-20 发布日期:2017-04-20
  • 通讯作者: 廖一兰 E-mail:chenhuiyan_12@163.com;liaoyl@lreis.cn.cn
  • 作者简介:

    作者简介:陈会宴(1991-),女,河南人,硕士生,主要从事时空分析方法研究。E-mail:chenhuiyan_12@163.com

  • 基金资助:
    国家自然科学基金项目(41471377);国家自然科学基金创新群体项目(41421001);资源与环境国家重点实验室自主创新青年基金项目

Spatial and Temporal Analysis of Neural Tube Defects in Yuanping County, Shanxi Province

CHEN Huiyan1,2(), LIAO Yilan2,*(), ZHANG Ningxu2,3, XU Bing2   

  1. 1. The School of Earth Science and Resources, Chang’an University, Xi’an 710054, China
    2.The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. University of Chinese Academy of Science, Beijing 10049, China
  • Received:2016-10-13 Revised:2017-01-16 Online:2017-04-20 Published:2017-04-20
  • Contact: LIAO Yilan E-mail:chenhuiyan_12@163.com;liaoyl@lreis.cn.cn

摘要:

神经管畸形是发生于中枢神经系统的一种先天性异常,在所有的新生儿出生缺陷中所占比例较高。在我国,作为矿业大省的山西,神经管畸形最为严重,原平市又是山西省出生缺陷的高发市之一。本文利用山西省原平市2007-2012年的神经管畸形病例资料,基于贝叶斯理论的时空建模方法,综合考虑时间组分和时空交互组分,对神经管畸形的时空规律进行研究。研究识别了研究区内疾病发生的热点区域、冷点区域、温点区域,并对这些区域随时间的变化趋势进行了分析。研究发现,原平市18个乡镇中有1个热点区域、17个温点区域,整体发病率较高;原平市神经管畸形整体上随时间变化呈缓慢下降趋势,但该趋势并不明显;1个热点区域疾病风险下降趋势慢于整体趋势,4个温点区域疾病风险下降趋势快于整体趋势,13个温点区域疾病风险时间变化趋势与整体趋势趋同。本文识别了山西省原平市神经管畸形发病的时间趋势和空间趋势,可以揭示神经管畸形潜在的风险因子或控制措施供进一步流行病学研究,也可以为公共卫生部门制定及时有效的防治控制措施提供一定的科学参考。

关键词: 原平市, 神经管畸形, 贝叶斯时空模型, 时空交互, 时间趋势

Abstract:

Neural tube defects (NTDs) are congenital anomalies that occur in the central nervous system. NTD is one of the birth defects with the highest incidence. China has the world’s highest rate of NTDs. Moreover, Shanxi province which is a leading producer of coal in China, has the Chinese highest incidence of NTDs. Yuanping County is one of the cities with highest incidence of NTDs. In epidemiology, researchers often use data based on the spatial distribution of diseases. However, with the growing interest in detection of variation of temporal trend in different study units, spatial-temporal modeling has been developed in the epidemiological analysis. Recently, one of spatial-temporal models based on the theory of bayesian has been extensively applied to the analysis of spatio-temporal patterns in relation to given diseases. The main difference of Bayesian spatial-temporal model is that it offers a natural framework to combine information from neighbouring areas or periods and hence to make the estimated results more reliable. In this paper, we applied a Bayesian spatial-temporal model and incorporated a space-time interactions component to explore the spatial-temporal variation of NTDs. The incidences of NTDs in Yuanping County of Shanxi Province between 2007 and 2012 were selected to analyze the spatial-temporal variation. Firstly, we identified areas that belong to the hot spots, cold spots or neither, and then studied the temporal trends of each area. Results show that the incidence rates of NTDs in Yuanping County is still very high. There is 1 hot spot, none cold spot and 17 areas that are neither hot spots nor cold spots. As a whole, the risk of NTDs in Yuanping County is slowly decreasing. The single hot spot has a slower decreasing trend compared to the overall decreasing trend in Yuanping County. Four areas which are neither hot spots nor cold spots show a faster decreasing trend. The rest of thirteen areas show the same decreasing trend as the whole. This paper identified the space-time variation and trends of NTDs in Yuanping County, which can help to study the potential factors and control measures of NTDs. Also, we provide scientific basis for the government to prevent the occurrence of NTDs.

Key words: Yuanping County, neural tube defects, Bayesian spatial-temporal model, space-time interactions, temporal trend