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地球信息科学学报 >

2017 , Vol. 19 >Issue 4: 502 - 510

DOI: https://doi.org/10.3724/SP.J.1047.2017.00502

地理空间分析综合应用

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
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  • 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
*通讯作者：廖一兰（1980-）,女,湖南邵阳人,博士,副研究员,主要从事时空分析理论与方法研究。E-mail:

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:

收稿日期: 2016-10-13

  要求修回日期: 2017-01-16

  网络出版日期: 2017-04-20

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

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

  • CHEN Huiyan , 1, 2 ,
  • LIAO Yilan , 2, * ,
  • ZHANG Ningxu 2, 3 ,
  • XU Bing 2
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  • 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
*Corresponding author: LIAO Yilan, E-mail:

Received date: 2016-10-13

  Request revised date: 2017-01-16

  Online published: 2017-04-20

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《地球信息科学学报》编辑部 所有
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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

本文引用格式

陈会宴 , 廖一兰 , 张宁旭 , 徐冰 . 山西省原平市神经管畸形时空分析[J]. 地球信息科学学报, 2017 , 19(4) : 502 -510 . DOI: 10.3724/SP.J.1047.2017.00502

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

1 引言

出生缺陷（Birth Defects,BD）即先天异常（Congenital Anomalies,CA）,指胚胎或胎儿在发育过程中发生的解剖学和功能上的异常[1],根据世界卫生组织2000-2013年儿童死亡原因整理,出生缺陷为全球新生儿期死亡的第五大主要原因。在欧洲,高达25%的新生儿死亡病例是出生缺陷所致[2]。中国每年新增出生缺陷儿约90万例,全国监测数据显示,中国围产儿出生缺陷发生率呈明显上升态势,由2000年的109.79万升高到2013年的145.06 万[3]。出生缺陷不仅会引起新生儿死亡,也会造成不同类型的残疾,给个人、家庭造成无法衡量的心理负担和精神痛苦,也给社会带来沉重的负担[4]。因此,对出生缺陷的研究已经成为各个领域研究的热点问题。
在出生缺陷研究中,以空间分析手段进行疾病的空间信息研究可以探索出生缺陷的潜在发病模式以及环境致病因子、估计发病风险等,能够为认识出生缺陷的发生发展和实施人工干预手段提供科学指引[5]。Edwards于1958年对苏格兰地区的出生缺陷病例进行疾病制图,发现疾病分布存在明显的空间差异,并且通过相关性分析发现无脑儿和脊柱裂之间存在明显的相关性[6]。Dolk等利用缓冲区分析,发现离化学垃圾填埋场不到3 km范围内的母亲生育出生缺陷儿的风险增加,且通过逻辑回归与二项回归检验的模型表明距离化学垃圾填埋场越远,患病风险越小[7]。1998年Dolk又探究了英国1988-1994年出生缺陷病病例的聚类情况,采用Cuzick-Edwards和Diggle-Chetwynd统计量进行检验,结果在国家水平2个统计量都没有显示显著性的局部地区聚集,在Trent的2 km范围内以及 Oxford的50 km范围内Diggle-Chetwynd统计量具有统计显著性[8]。相比于国外出生缺陷研究,中国在1987-1988年开展了覆盖全国的出生缺陷发生情况摸底调查,其成果也以中国出生缺陷地图集的形式得以发布,初步揭示了中国不同区域的出生缺陷发生的特点[9]。近年来,关于出生缺陷的研究越来越深入。迟文学等采用核密度估计和 Ripley’s K 函数进行空间点格局分析,发现山西省和顺县新生儿神经管畸形的空间分布格局存在聚类分布规律,尤其在研究区域的中部和东南部呈聚类分布,病例点在空间距离3.17~10.41 km 内聚类趋势显 著[10]。在此基础上,迟文学还应用空间扫描统计量来检测疾病聚类点的确切空间位置[11]。武继磊等通过小波分析对研究区域16年的出生缺陷发生率进行时间趋势分解,用全局G统计量检验不同距离水平下的空间特性,小波分析结果表明出生缺陷发生率随时间呈增长的趋势,不同的距离水平空间聚类水平不同[12]。廖一兰等利用基于非参数统计的空间滤波法识别采煤区是否存在出生缺陷发病异常聚类,发现在采煤区6 km的范围内存在疾病聚群[13]。
空间分析方法已广泛应用于出生缺陷研究中,但是相对于疾病的空间信息分析,疾病的时间信息对于预防控制部门进行决策的制定也非常重要。通过时空分析,不仅可以获取疾病的空间分布差异,还可以了解疾病随时间的动态变化趋势。基于疾病的时空分析自提出以来处于不断发展的阶段,其中基于贝叶斯理论的时空分析方法已经成为疾病资料的时空分析的热点研究[14]。贝叶斯时空模型是在贝叶斯统计思想的框架下,为分析时空数据资料中蕴含的时间和空间信息而建立的数学模型,是空间模型的扩展。神经管畸形（Neural tube defects,NTDs）是一种在中国发生率非常高、后果非常严重的出生缺陷[4]。山西省新生儿出生缺陷,尤其是神经管畸形的发生在全国乃至世界处于较高水平,其中原平市是山西省出生缺陷的高发市之 一[15]。因此,本文选择基于贝叶斯理论的时空建模方法,选取原平市2007-2012年神经管畸形病例为研究对象,探究研究区域内疾病的发生在时间和空间上的变化规律。

2 研究区概况与数据源

2.1 研究区概况

本文的研究区选择山西省出生缺陷率较高的原平市[16],其位于山西省北部（38°35' ~39°09' E,112°17'~ 113°35' N）,东西绵亘群山为历代之天然界域,阳武河、滹沱河畔是全市之开阔地带（图1）。现辖有7镇11乡和2个街道办事处,共有520个行政村,辖区面积2560 km2。总人口约49.76万,人口密度约182 人/km2。
Fig. 1 Location of the study area

图1 研究区地理位置图

原平市地形为山地型高原,地面标高在海拔 800~2385 m之间,总的地形地貌特征为东西环山,中部为冲积盆地,整个地势西高东低,中部地势平坦。原平市境内的河流以滹沱河最大,由北而南纵贯全县,流经沿沟乡、苏龙口镇、崞阳镇、中阳乡、西镇乡、子干乡、新原乡、王家庄乡8个乡镇,阳武河、永兴河、北云中河、长乐河和同河等也是该市主要河流。
原平资源富集,特别是轩岗等西山地区乡镇矿产资源十分丰富,有煤、铝、铁、石灰岩、钾长石、硅石、铜矿、粘土等20种,其中以煤、石英矿、铁矿居多,小型矿厂居多,主要集中于西北部的段家堡乡、轩岗镇、长梁沟镇以及东部的苏龙口镇、东社镇、南白乡、子干乡,并由此形成了原平市境内方便的交通网络。京原铁路、北同蒲铁路、朔黄铁路在城区交汇。京原、大运、乡宁等国道和原太高速公路交汇于城区,贯穿全市。

2.2 数据源

本文搜集的出生缺陷数据区分为2类：①神经管畸形（包括无脑儿、脊柱裂、脑膨出、脑积水等）;②非神经管畸形（包括足外翻、多/并指（趾）、食道闭锁、两性畸形、唇/腭裂等）。其中,神经管畸形（Neural Tube Defects,NTDs）是一种发生率非常高、后果非常严重的出生缺陷。在原平市,神经管畸形尤为突出,因此选择神经管畸形为研究对象。数据由原平市妇幼保健部门提供,包括原平市2007-2012年神经管畸形详细登记信息及2007-2012年原平市18个乡镇的出生人口数据。对神经管畸形病例数据,统计各年份各乡镇的病例总数,为保证数据的稳定性,在具体的时空分析中舍弃掉6年间乡镇神经管畸形总病例数少于3的病例。对出生人口数,2007-2012年各乡镇各年份出生人口数相差不大,对6年的出生人口数取平均数作为各乡镇的出生人口数。在ArcGIS 10.2中对病例数据进行空间定位,形成有空间定位信息的原平市神经管畸形矢量数据文件。每个乡镇有唯一编码,属性包括乡镇名称、出生人口数,各年份神经管畸形病例数。地理环境数据由原平市相关部门提供,主要有河流分布数据、铁路线数据、煤矿分布及各煤矿产量数据和铁矿、硅铝石矿分布数据,均通过ArcGIS 10.2建立相应的地理环境矢量数据文件。据统计,2007-2012年神经管畸形的平均发病数呈现先增长（从2007年的0.33至2008的1.89）后急剧下降（从2009年的1.22至2010年的0.61）的趋势,到2012年（0.39）又大致与2007年持平。

3 贝叶斯时空模型

贝叶斯时空模型基于贝叶斯统计思想,从空间模型扩展而来,用于分析时空数据资料中蕴含的时间和空间信息。区别于其他时空分析方法,其可以不受信息量多少的限制,将各种来源的结果,包括主观判断和有限的客观信息,综合未知参数的先验概率分布,得到关于参数的后验概率,使结果更为稳健,置信度高[17]。当其应用于疾病的时空分析时,模型中不仅可以考虑时间和空间主效应,还可以引入时空交互效应[18]。具体模型分为3个层次：
（1）数据模型。对于发病率较低的统计数据,假设服从参数为 n i 和 μ it 的泊松分布,如式（1）所示。
y it ~ Poiss ( n i μ it ) （1）
式中： y it 为发生于i（1,…,18）乡镇t（1,…,6）年的病例数。假定每个乡镇的风险人群数在研究时段内没有变化, n i 为i乡镇的风险人群数, μ it 为相应的i乡镇t年疾病发生风险[19]。
（2）过程模型。对疾病发生风险 μ it 实现对数变换,可使相对风险表达为空间组分、时间组分以及时空交互组分的线性组合。数学表达如式（2）所示。
log μ it = α + s i + （ b 0 t * + υ t ） + b 1 i t * + ε it （2）
式中： α 为整个研究区域6年的总体相对风险的固定效应; t * = t - 3.5 为相对于中间时间点的时间跨度,该模型中,疾病发生风险分解为空间变异、时间变异和时空交互3部分; s i 为空间变异组分,描述了疾病发生风险在观测6年间各乡镇的风险相对于整个研究区域的风险差异; b 0 t * + υ t 为时间变异组分,描述了疾病发生风险在整个研究区域上相对于中间观测年的整体变化趋势,包括线性趋势 b 0 t * 和时间随机效应 υ t , b 0 为时间系数,代表了在整个研究区域上的时间趋势; b 1 i t * 则允许各个乡镇存在不同的时间变化趋势,为时空交互部分。相对于 b 0 而言,其为各个乡镇基于 b 0 的局部变化趋势[20]; ε it 用于解释不能由空间随机效应和时间随机效应所解释的部分变异[21]。
（3）参数模型,即定义先验分布。空间结构效应,根据BYM（Besag York and Molliè）[22]模型,利用条件自回归（CAR）先验结构来界定。在该过程中需要定义一个空间邻接权重矩阵,相邻则权重 w ij = 1 , 否则权重 w ij = 0 , 特别的 w ii = 0 。同样, b 1 i 也假定服从BYM过程。对时间结构效应 υ t ,也用条件自回归过程进行拟合,同理定义时间上的邻接权重矩阵。对于过度离散参数 ε it ,根据Gelman[21]服从均值为0,方差为 σ ε 2 的正态分布。各个参数方差通常假定服从Gamma（a,b）。
基于该模型,首先通过空间组分 s i 及其后验概率,可以识别出相对于整个研究区域平均风险（α）的高风险或低风险乡镇。根据Richardson等[23]提出的用于识别高风险或低风险区域的决策规则,计算空间组分 s i 大于0的概率,exp（si）大于1的概率大于0.7的乡镇定义为热点乡镇,概率小于0.2的乡镇定义为冷点乡镇,概率介于0.2和0.7之间的为温点乡镇。基于这一分类结果,同理根据概率阈值,可以识别出这些分类区域基于整个时间变化趋势的差异。将exp（b1i）大于1的概率分为3类：概率大于0.7的乡镇认为其发病风险相对于总体风险变化有快速变化的趋势;概率小于0.2的乡镇认为其发病风险相对于总体风险变化有减弱的趋势;概率介于0.2和0.7之间的认为其风险变化趋势与整体风险变化趋势大体一致。注意到这种增强或减弱的趋势是相对总体时间趋势而言的,因此并非绝对意义上的疾病风险的增加或减少。基于上述两步分类,可以将整个研究区域分为3×3=9类,热点区域风险变化趋势较快,热点区域风险变化趋势较慢,热点区域风险变化趋势与整体变化趋势一致,冷点区域风险变化趋势较快,冷点区域风险变化趋势较慢,冷点区域风险变化趋势与整体变化趋势一致,温点区域风险变化趋势较快,温点区域风险变化趋势较慢,温点区域风险变化趋势与整体变化趋势一致。类型定义如表1所示。
Tab. 1 Categories of disease risks trend for each towns

表1 各乡镇疾病风险趋势类型表

增强 减弱 趋同
热点 ++ +- +/
冷点 -+ - - -/
温点 /+ /- //

注：表中符号为AB型结构,其中A部分的“+”、“-”、“/”依次表示热点、冷点、温点,B部分的“+”、“-”、“/”依次表示增强、减弱、趋同。例如“++”表示热点区域疾病风险变化趋势较快,“+-”表示热点区域疾病风险变化趋势较慢或者有与整体变化趋势相反的变化趋势

4 结果与分析

WinBUGS软件是专门用于贝叶斯统计分析的软件[24]。模型并行运行两条链,共迭代8万次后模型趋于收敛。模型收敛后,又迭代2万次用于参数估计。模型的收敛性通过Gelman-Rubin 统计量[25]来判别。该方法与经典方差分析方法类似,通过比较链间和链内方差来判断模型的收敛情况。图2为BRG收敛诊断图,参数从左至右从上到下依次为疾病风险（μit）,疾病风险的方差 （σ2μ）,空间变异组分的方差（σ2s）,时间变异组分的方差（σ2b1）。图中蓝线表示待估计参数的链间方差,绿线表示链内方差,红线为2者方差之比,比值接近于1表示2条链迭代序列接近,模型收敛状况良好,趋于稳定[26]。
Fig. 2 Convergence diagnostics plots of Gelman-Rubin

图2 Gelman-Rubin收敛诊断图

模型估计结果中空间相对风险后验均值如 图3（a）所示,由模型中exp（si）计算得到,为空间变异组分,描述的是在观测6年间各乡镇间的疾病发生风险差异。由图3（a）可知原平市中部、南部以及东部大部分在2007-2012年的发病风险较高,均大于1。依据上文所述的分类机制,通过计算空间相对风险的概率估计,将exp（si）大于1的概率分为3类：概率大于0.7的乡镇定义为热点乡镇;概率小于0.2的乡镇定义为冷点乡镇;概率介于0.2和0.7之间的为温点乡镇。其中,热点乡镇为疾病风险高于整个研究区域平均发病风险的乡镇,冷点乡镇为疾病风险低于整个研究区域的乡镇,温点乡镇疾病发病风险与整个研究区域平均发病风险保持趋同。概率估计如图3（b）所示,根据计算结果,有74.11%的概率认为西镇乡为疾病风险的热点乡镇,其余的17个乡镇均为温点乡镇,没有冷点乡镇。不难发现,原平市神经管畸形的高发区域西镇乡,同时处于原平市境内的河流交汇处以及以运煤为主的铁路线交汇处。原平市是山西省重要的能源和工业基地,截止2008年底市域范围内遍布大大小小的煤矿34家（山西省原平市矿务局）,煤炭资源在开采、加工、运输和利用等过程中都伴随着一系列的环境问题。例如在煤炭开采和加工过程中产生的扬尘以及散发的伴生元素不仅会对矿区周边的自然环境产生影响,在煤炭运输过程中会随运煤通道、空气流动、水体迁徙等途径影响到主要道路周边的环境,而在煤的燃烧过程又会产生大量的烟尘会对环境造成严重污染,局部地区环境的污染必然会对人体健康造成负面影响[27]。此外,采煤过程中会排放大量废水,市营以下的小型矿井几乎都没有配套的矿井水处理设施,废水的直接排放无疑会严重影响水体环境,从而直接或间接地引发各种地方健康问题,而由于饮用水污染导致的地方疾病如神经管畸形已成为人类健康的主要负担[28]。据统计,2007-2010年原平市化学需氧量排放均居山西城市前三,而在原平市域范围内,包括化学需氧量在内的废污水排放主要集中在水体交汇的城区及矿井聚集的轩岗镇[29],这与探测出来的神经管畸形热点区域为西镇乡所在区域相吻合,也在一定程度上说明原平市水体污染或许是原平市神经管畸形的风险要素之一。
Fig. 3 The posterior means and the posterior probabilities of the spatial relative risks (exp(si)) in Yuanping County

图3 原平市空间相对风险(exp(si))后验均值和后验概率

模型估计结果中的总体时间变化趋势由模型中（exp（ b 0 t * + υ t ））计算得到,描述的是2007-2012年原平市整体发病风险随时间的变化,如图4所示。可见,2007-2008年原平市的疾病发生风险呈增长趋势,从2008年以后,疾病发生风险逐年下降。其中,平均时间趋势系数 b 0 的参数估计为-0.02066,表示相邻两年间疾病风险的平均变化为exp( b 0 ),即后一年的疾病风险大约是前一年疾病风险的0.98倍,因此整个原平市6年总体疾病风险呈现下降趋势,但下降趋势很缓慢。
Fig. 4 The overall time trend in Yuanping County

图4 原平市总体时间变化趋势

事实上依据2010年9月山西省人民政府批转了关于山西省煤矿企业兼并重组整合工作领导组办公室《关于加强兼并重组整合矿井安全工作通知》,原平市已于2010年7月底前关闭矿井20个。而贝叶斯时空模型中的时间趋势中2011年的下降明显快于2010年,在一定程度上说明关闭矿井措施对减少原平市神经管畸形的发生起到了一定的积极作用。
为了清楚地了解各个乡镇6年来疾病风险的时间变化趋势,由模型中exp（b1i）计算得到后验均值,描述了各个乡镇疾病风险相对于整体下降趋势的变异,结果如图5（a）所示。其中,西镇乡、苏龙口镇、子干乡、新原乡、中阳乡、南白镇、崞阳镇、大牛店镇、东社镇疾病风险在6年间的变化系数为负数,其疾病风险在6年间下降缓慢,而长梁沟镇及其周边几个乡镇包括轩岗镇、楼板寨乡、闫庄镇等乡镇,其疾病风险在6年间的变化系数为正数,则表现为快速下降的趋势。为了得到更精确的局部时间变化趋势,同理计算exp（b1i）大于1的概率,其概率估计如图5（b）所示。根据计算结果,将exp（b1i）大于1的概率分为3类：概率大于0.7的乡镇认为其发病风险相对于总体风险的下降有快速下降的趋势;概率小于0.2的乡镇认为其发病风险有缓慢下降的趋势;概率值介于0.2 和0.7之间的认为其发病风险下降趋势同总体的下降趋势保持一致。具体而言,长梁沟镇、轩岗镇、楼板寨乡、闫庄镇疾病风险局部变化系数均为正值,变化系数的后验概率大于0.7,理解为有超过70%的概率可以认为其疾病风险有快速下降的趋势。西镇乡空间稳定组分风险最大,为1.189,后验概率为0.7411大于0.7,判定其为热点区域。而其局部变化趋势（b1i）-0.156小于0,局部变化趋势的后验概率为0.1542,小于0.2,因而其疾病风险下降的趋势缓慢。其余的13个乡镇疾病风险保持与整体一致的下降趋势。上述5个乡镇详细的时间变异及空间变异值如表2所示。
Fig. 5 The posterior means and the posterior probabilities of the departures of the local trends from the overall trend (exp( b1i )) in Yuanping County

图5 原平市各乡镇相对总体风险变化趋势的局部趋势 (exp(b1i))的后验均值和后验概率

Tab. 2 The posterior means and posterior probabilities of the spatial relative risks and the departures of the local trends from the overall trend for specific areas

表2 特定区域时间及空间变异的后验均值和后验概率

Area（i） si pp（si） b1i pp（b1i）
西镇乡 1.1890 0.7411 -0.1560 0.1542
长梁沟镇 0.9344 0.3668 0.3826 0.9375
轩岗镇 1.0160 0.4658 0.1469 0.7764
楼板寨乡 0.9323 0.3589 0.1176 0.7343
闫庄镇 1.079 0.551 0.1062 0.7026
据此,按照上文所述的各乡镇疾病风险趋势类型表将原平市18个乡镇进行疾病风险趋势类别划分,如表3所示,原平市1个热点区域,并且其疾病风险表现为缓慢下降趋势,4个温点区域表现为快速的下降趋势。
Tab. 3 Numbers of areas belong to each disease risks trend category

表3 原平市各疾病风险趋势类别的乡镇数量统计表

增强 减弱 趋同 总计/个
热点 0 1 0 1
冷点 0 0 0 0
温点 4 0 13 17
总之,原平市6年间神经管畸形发病风险总体表现为下降趋势,这说明原平市采取了一定的干预措施,也取得了一定的积极效果。但是各个乡镇下降程度大不相同,可能是由于不同乡镇、不同村庄的社会经济和地理环境不同,使得干预措施所起效果也不尽相同。为提高采煤污染区出生缺陷干预措施的普及度和公平性,需要进一步详细探究各个乡镇主要的环境风险因子,据此给出相应的出生缺陷干预措施以提高出生人口质量。

5 结论

本文应用贝叶斯时空模型对山西省原平市2007-2012年神经管畸形发病情况进行了时空探索,从时间、空间、时空交互3个角度分析了原平市神经管畸形病例中所蕴含的时间信息和空间信息,并且基于贝叶斯的统计思想,充分利用所认识疾病的先验信息,从而得到后验信息,使结果更可靠。该模型以其独特的优势不仅能够给出研究对象在研究区域的热点区域,还能够给出特定区域在研究时间段内的变化趋势。发现原平市18个乡镇中有一个乡镇疾病风险高于全市平均水平,4个乡镇疾病风险与全市平均水平保持一致。整个原平市的时间趋势为缓慢下降趋势,但下降趋势并不明显。虽然有一个热点乡镇疾病风险下降趋势慢于整体趋势,但还有4个温点乡镇下降趋势快于整体下降趋势,其余13个表现为温点的乡镇疾病风险与整体的风险变化趋势大体一致。
根据以上分析结果,建议公共卫生部门在制定相应的预防控制措施时应该充分考虑各个乡镇间的差异及布局变化趋势,因地制宜的实施预防控制措施有助于资源的优化配置。对于疾病风险下降趋势慢于整体趋势的乡镇要给与重点监测防控;而对于疾病风险下降趋势快于整体趋势的乡镇而言,虽然不用作为防控的重点,但也不能掉以轻心。然而,本文没有考虑环境风险要素对于原平市神经管畸形发病的影响,因此下一步考虑将各个乡镇的环境风险因素加入模型中,进一步探究引发各个乡镇局部变化趋势的风险因子,为公共卫生部门进行神经管畸形的病因学研究提供科学参考,以及为预防控制部门制定相应的预防控制措施提供决策支持。

The authors have declared that no competing interests exist.

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Li Z W, Ren A G, Zhang L, et al.Extremely high prevalence of neural tube defects in a 4-County area in Shanxi Province, China[J]. Birth Defects Research(Part A), 2006,76(4):237-240.BACKGROUND: In the past, northern China's Shanxi Province has reported the highest incidence of neural tube defects (NTDs) in the world. However, little is known about the epidemiology of NTDs in this area in recent years. METHODS: Data were collected from a population-based birth defects surveillance system in 4 counties that captures information on all live births, stillbirths of at least 20 weeks' gestation, and pregnancy terminations at any gestational age resulting from prenatal diagnosis of a birth defect. We also surveyed mothers of NTD case patients to determine their use of folic acid before and during early pregnancy. RESULTS: During 2003, 160 NTD cases were identified among 11,534 births (NTD birth prevalence = 138.7/10,000 births). The rates of anencephaly, spina bifida and encephalocele were 65.9, 58.1, and 14.7 per 10,000, respectively, and a female predominance was observed among anencephaly cases (male-to-female relative risk [RR], 0.49; 95% confidence interval [CI], 0.30-0.79), but notamong spina bifida (RR, 0.90; 95% CI, 0.55-1.45) and encephalocele (RR, 1.03; 95% CI, 0.40-2.69) cases. The percentages of pregnancy termination following prenatal diagnosis of anencephaly, spina bifida, and encephalocele were 50%, 41.8%, and 35.3%, respectively. NTD birth prevalence tended to be higher among mothers aged or =30 years (P .06) and was markedly associated with lower levels of maternal education (P < .001). Among 143 NTD mothers, only 6 (4.2%) used folic acid supplements during the periconceptional period. CONCLUSIONS: The NTD birth prevalence rate in the study area is among the highest worldwide. Folic acid deficiency may be one important risk factor.

DOI PMID

[17]
李俊明. 基于Bayesian层次时空模型的我国老龄化分析与预测[J].统计研究,2016,33(8):89-94.本文首次利用Bayesian层次时空模型,以1995—2014年全国省级人口统计数据为基础,分析了近20年来我国老龄化在空间和时间上的变化规律。研究发现:1我国高老龄化地区分布已形成X型地理空间分布结构,东部地区为主,西部地区为辅,总体老龄化率呈上升趋势;2四川、重庆、辽宁、安徽、湖北和湖南等6个地区不仅是老龄化热点区域,而且老龄化增速也快于全国平均水平,特别是四川和重庆,老龄化程度和增速都是全国最高;3中西部地区老龄化程度虽然低于全国平均水平,但增加速度却高于全国平均水平;4北京、天津、上海、江苏、浙江和广东等6个高老龄化地区的老龄化率趋于平稳或增速放缓;5预测"全面二孩"政策情境下我国2030年老龄化率为13.19%(11.10%,20.94%)。

DOI

[Li J M.Space-time variation of Chinese aging based on bayesian hierarchy spatio-temporal model[J]. Statistical Research, 2016,33(8):89-94. ]

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[Ren Z P.Impacts of climate change on dengue fever in China-southeast Asian countries[D]. Beijing: The university of Chinese academy of science, 2016. ]

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[Jin D S.Strategy research on the lang reclamation and ecological reconstruction in coal mining area in shanxi[D]. Taiyuan: Shanxi University, 2009. ]

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