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地球信息科学学报 >

2016 , Vol. 18 >Issue 12: 1615 - 1623

DOI: https://doi.org/10.3724/SP.J.1047.2016.01615

地理空间分析综合应用

京津唐地区细菌性痢疾社会经济影响时空分析

  • 李媛媛 , 1, 2 ,
  • 徐成东 , 2, * ,
  • 肖革新 3 ,
  • 罗广祥 1
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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.国家食品安全风险评估中心,北京 100022
*通讯作者：徐成东（1982-）,男,山东人,博士,助理研究员,研究方向为空间分析。E-mail:

作者简介：李媛媛（1990-）,女,山西人,硕士生,研究方向为空间分析。E-mail:

收稿日期: 2016-07-30

  要求修回日期: 2016-10-15

  网络出版日期: 2016-12-20

基金资助

中科院战略先导专项子题: 应对气候变化的碳收支认证及相关问题/100年来区域年均温估算（XDA05090102)

国家“973”计划项目：气候变化对人类健康的影响与适应机制/气候—健康脆弱人群识别和风险区划（2012CB955503）

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Spatial-temporal Analysis of Social-economic Factors of Bacillary Dysentery inBeijing-Tianjin-Tangshan, China

  • LI Yuanyuan , 1, 2 ,
  • XU Chengdong , 2, * ,
  • XIAO Gexin 3 ,
  • LUO Guangxiang 1
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  • 1. The School of Earth Science and Resources, Chang′an University, Xi′an 710054, China;
  • 2. Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System,Beijing 100101, China;
  • 3. China National Center for Food Safety Risk Assessment, Beijing 100022, China
*Corresponding author: XU Chengdong, E-mail:

Received date: 2016-07-30

  Request revised date: 2016-10-15

  Online published: 2016-12-20

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《地球信息科学学报》编辑部 所有
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摘要

细菌性痢疾是常见疾病,也是备受关注的公共健康问题。近年来,京津唐地区的细菌性痢疾发病率相对较高。本文首先分析了2012年京津唐地区细菌性痢疾的季节性和人群特征;其次,使用热点分析模型,探索了京津唐地区细菌性痢疾发病率的时空聚集性;最后,运用地理探测器模型研究了细菌性痢疾的发生和社会经济因素之间的量化关系。结果表明：① 细菌性痢疾发病的峰值时间是8月;发病率最高的年龄段是0-9岁,其次是80岁以上;农民群体发病率最高,其次是散居儿童。② 京津唐地区细菌性痢疾在空间和时间上都存在聚集性。空间上,细菌性痢疾发病率的高聚集区主要分布于北京市的房山区及门头沟区和天津市的滨海新区,低聚集区主要分布于唐山市的滦县,时间上,细菌性痢疾发病率的高聚集区在12个月均有发生,低聚集区主要发生在1-4月以及6月。③ 影响细菌性痢疾发病率空间分布的主要社会经济因素为农村人口占总人口的比例、人口密度和各区县的人均GDP,它们的解释力分别为61%,37%和20%,并且发现它们的交互作用都大于独自影响的作用。本研究通过对京津唐地区细菌性痢疾发病情况的人群特征、时空特征以及影响因素的分析,为本地区细菌性痢疾的预防和控制提供理论依据。

关键词： 地理探测器; 细菌性痢疾; 社会经济因素; 时空分布

本文引用格式

李媛媛 , 徐成东 , 肖革新 , 罗广祥 . 京津唐地区细菌性痢疾社会经济影响时空分析[J]. 地球信息科学学报, 2016 , 18(12) : 1615 -1623 . DOI: 10.3724/SP.J.1047.2016.01615

Abstract

Bacillary dysentery is a common disease as well as a public health problem with much attention. In recent years, the incidence of bacterial dysentery is rather prevalent in Beijing-Tianjin-Tangshan region. This paper analyzed the seasonal and population characteristics of bacillary dysentery in Beijing-Tianjin-Tangshan region in 2012 firstly. Then, we explored the spatial and temporal clustering of the incidence of bacillary dysentery by using hotspot analysis model. We also investigated the quantitative relationship between the incidence of bacterial dysentery and the social-economic factors by using geographical detector model. The results showed that: (1) the peak attack time of bacillary dysentery was August. The age range that had the highest incidence was 0-9 years old, followed by those above 80 years old. The population that had the highest incidence was farmers, followed by the scattered children. (2) The incidence of bacterial dysentery clustered in both space and time in Beijing-Tianjin-Tangshan region. In space, the high clustering regions for incidence of bacillary dysentery are mainly located in Fangshan District and Mentougou District of Beijing and Binhai New Area of Tianjin;the low clustering regions are mainly located in Luan county of Tangshan. In time, the disease occurred in all the 12 months in 2012 in the high clustering regions, but mainly occurred in January, February, March, April and June in the low clustering regions. (3) The major socio-economic factors affecting the spatial distribution of incidence of bacterial dysentery included the proportion of rural population, population density and per capita GDP of each district or county, which explanatory power was 61%, 37% and 20%, respectively. The interactive effects were stronger than their individual effects. This study analyzed the population characteristics, spatial and temporal characteristics and influencing factors of incidence of bacillary dysentery in Beijing-Tianjin-Tangshan region and provided a theoretical basis for the prevention and control of bacterial dysentery in these regions.

Key words： GeoDetector; bacillary dysentery; socio-economic factors; spatial-temporal distribution

1 引言

细菌性痢疾是由志贺菌属（痢疾杆菌）引起的肠道传染病,是常见病、多发病。它的临床表现主要有发热、腹痛、腹泻等,其中中毒性细菌性痢疾起病急骤,迅速发生循环衰竭和呼吸衰竭,而肠道症状轻或无,病情凶险。细菌性痢疾主要是通过粪-口途径、非感染者与感染者、感染者与感染者之间的接触等传播。无论是发达国家还是发展中国家,细菌性痢疾都是一个备受关注的公共健康问题[1-2]。
中国细菌性痢疾的疾病负担依然严峻[3-4]。2009年,细菌性痢疾的新增病例数为269 703例,其中发病率最高的是北京（142.78/10万）,发病率最低的是江苏省和广东省[5];2011年,细菌性痢疾的新增病例数为236743例,发病率最高的仍是北京（132.37/10万）,最低的是福建省和上海[6];2012年,细菌性痢疾的新增病例数为205 972例,研究发现细菌性痢疾的报告发病率居前5位的省（自治区、直辖市）依次是北京（65.27/10万）、天津（63.76/10万）、西藏（42.07/10万）、甘肃（35.96/10万）和新疆（32.89/10万）[7]。京津唐位于华北平原东北部,华北地区与东北地区间的结合部,是聚集竞争力最高、发展最快的都市经济圈之一,也是中国人口密度最大的地区之一。近年来,京津唐地区细菌性痢疾的发病率相对较高[8-11]。
许多研究表明,细菌性痢疾的发生与气象因素有直接的关系。例如,Zhang等研究了中国北部的济南市和南部的深圳市宝安区的细菌性痢疾与气象因素的关系,结果显示温度、降水、相对湿度、大气压和细菌性痢疾的发病有显著的相关性[12];Gao等运用ARIMAX模型分析了长沙市细菌性痢疾与气象因素的关系,发现月平均温度、月平均最高温和月平均最低温每升高1 ℃,分别对应的细菌性痢疾发病率会增加14.8%,12.9%和15.5%[13];Huang等在使用岭回归和聚类分析研究沈阳市细菌性痢疾的发病率和气象数据的关系中发现,温度、降水、蒸发、相对湿度和细菌性痢疾的月发病率都呈正相 关[14]。而目前社会经济因素对细菌性痢疾影响的研究还很少[14-15]。
在细菌性痢疾影响因素的研究中,目前主要使用的方法有空间面板模型、岭回归和聚类分析、广义加性时间序列模型等。这些方法仅研究了气象因素、社会经济因素对细菌性痢疾发病的影响以及影响因素和细菌性痢疾发病之间的相关性,对于各影响因素之间的相对重要性及交互作用尚未研究;其次,细菌性痢疾的发生具有明显的空间异质性,这在以往的方法中尚未考虑。地理探测器模型是一种可用于探测地理要素空间格局、分析其机理的重要方法,此模型基于空间分异理论和空间方差分析,充分考虑研究对象的空间异质性,探测各影响因素的解释力,揭示因素间的交互作用,目前已广泛用于探测疾病的风险因素。近年来,地理探测器模型也逐渐应用于经济[16]、地质灾害[17]、动物生境评价[18]等诸多领域。故本文运用地理探测器模型,来探测社会经济因素和细菌性痢疾之间的关系。本文明确了京津唐地区细菌性痢疾发病的人群特征、时空特征,论证了社会经济因素对细菌性痢疾的影响,分析了它们之间的相关性,也明确了各社会经济因素对细菌性痢疾的解释力,以及各社会经济因素之间对细菌性痢疾的交互作用,为本地区细菌性痢疾的预防和控制提供了理论依据。

2 研究数据与方法

2.1 数据

研究区包括京津唐地区的36个区县,采用的数据是2012年京津唐地区各区县的细菌性痢疾发病数据,总病例数为2322例;细菌性痢疾发病数据包括个体病例的发病时间、地理位置以及年龄、职业等信息。
社会经济因素的研究中,不同地区选取的指标不同[14-15]。在京津唐地区,各区县的地区生产总值与财政支出有很强的相关性（p<0.01）,地区生产总值反映了一个地区的经济发展水平,而财政支出主要是财政在公共产品和服务等方面的分配,并且财政支出包涵的应用项目众多,与细菌性痢疾相关的只占很小的一部分,故在二者之间选择各区县的地区生产总值,并取人均值。所以本文选取以下社会经济指标：第一产业比重、第二产业比重、各区县的人均地区生产总值;人口密度和农村人口占总人口的比例作为人口统计学指标加入到研究中。社会经济数据主要来源于2012年京津唐地区的统计年鉴。
图1展示了细菌性痢疾发病率和社会经济指标的空间分布。细菌性痢疾发病率较高的地区主要分布在北京市市辖区、门头沟区、房山区、延庆区、平谷区以及天津市市辖区、北辰区。京津唐地区各区县的人均GDP分布很不均,最高的是27.33万元,最低的是2.64万元,人均GDP较高的区域主要位于北京市市辖区、天津市滨海新区和唐山市的北部地区（图1(a)）;京津唐地区的东北部农村人口较多,农村人口占总人口的比例呈现东高西低的现象（图1(d)）;从图1(e)可看出,人口主要集聚在市中心。
Fig. 1 The geographical distribution of bacillary dysentery morbidity and social-economy indices

图1 细菌性痢疾发病率和社会经济指标的地理分布图

2.2 地理探测器

地理探测器既可以检验单变量的空间分层异质性,也可以通过检验2个变量空间分布的一致性,来探测2个变量之间可能的因果关系。地理探测器的基本思想是：结合GIS空间叠加技术和集合论,以“解释力（Power of Determinant）”作为度量指标,来识别潜在影响因素与健康指标之间的关系。
地理探测器主要由4部分组成：风险探测器、因子探测器、生态探测器和交互作用探测器。其基本原理[19-21]如图2所示。将地理空间（即京津唐地区）记为A,健康风险指标即本文研究的细菌性痢疾发病率的空间分布记为B。整个研究区可以被分成N个规则格网单元,并把每个格网单元中的细菌性痢疾发病率记 b i 1 ≤ i ≤ N ) 。C、D表示2个潜在影响因素, c i ( d i ) 1 ≤ i ≤ n c 1 ≤ i ≤ n D ) ) 表示影响因素C（D）的空间类别分区,其中每个子区域包涵 n c , z ( n D , Z ) 1 ≤ Z ≤ n c 1 ≤ Z ≤ n D ) ) 个格网单元 ( N = ∑ Z = 1 n C n C , Z ) 。在每个子区域中,每个格网单元的细菌性痢疾发病率记为 b z , i 1 ≤ Z ≤ n c , 1 ≤ i ≤ n c , z ) 。地理探测器是将细菌性痢疾发病率图层（即B层）与影响因素层（以C层为例）作空间叠加,以此来计算影响因素空间分区内细菌性痢疾发病率的均值和方差。
Fig. 2 Overlay of bacillary dysentery morbidity stratum and influencing factors stratum

图2 细菌性痢疾发病率层与影响因素层的叠加

（1）风险探测器。假设影响因素C可分成3个子区域,分别为c1,c2,c3。以子区域c1为例,子区域c1的细菌性痢疾发病率的均值 b c 1 ¯ 和 σ c 1 2 方差分别如式（1）、（2）所示。接着对影响因素C的不同空间类别分区c1,c2,c3之间进行发病率均值差异的显著性检验。均值显著大的分区,其发病率高,以此来探索细菌性痢疾的发病风险区。
b c 1 ¯ = 1 n c 1 ∑ i = 1 n c 1 b c 1 , i （1）
σ c 1 2 = 1 n c 1 ∑ i = 1 n c 1 ( b c 1 , i - b c 1 ¯ ) 2 （2）
（2）因子探测器。利用各空间分区的方差以及总方差计算各影响要素对细菌性痢疾发病率的解释力,即分辨出对细菌性痢疾发病率起到关键作用的影响因素,其解释力计算公式如式（3）所示。
q C , B = 1 - ( n c 1 Va r c 1 + n c 2 Va r c 2 + n c 3 Va r c 3 ) NVa r c （3）
式中：C为影响因子;B为细菌性痢疾发病率;Var表示方差; q C , B 为C对B的解释力 ( N = n c 1 + n c 2 + n c 3 ) 。
按照影响因素C的类别分区,细菌性痢疾的发病率在各个不同类别分区内的方差等于零时, q C , B = 1 ,则称该影响因素能很好的解释细菌性痢疾发病率的空间分布,即该因子对细菌性痢疾发病率的影响最大。
（3）生态探测器。比较各影响要素间细菌性痢疾发病率总方差的差异,探究不同的要素在影响疾病的空间分布方面的作用是否有显著的差异。
（4）交互作用探测器。通过比较 q C ⋂ D , B 、 q C , B 、 q D , B 的大小（其中,C、D代表潜在的影响因素, q C ⋂ D , B 表示C和D的交互作用对健康指标即细菌性痢疾的解释力）,识别影响因素之间的交互作用。

3 结果分析

3.1 季节性和人群特征

京津唐地区细菌性痢疾的发生存在很强的季节性,8、7、6、9月依次为细菌性痢疾病例数最多的4个月份（图3）,病例数分别为3746、3509、3155、2746例,占2012年该地区病例总数的57.59%,2月为发病数最少的月份,发病625例。
Fig. 3 The distribution of the cases of bacillary dysentery in Beijing-Tianjin-Tangshan in 2012

图3 2012年京津唐地区各月细菌性痢疾的病例数分布

细菌性痢疾的发病率在不同职业类型中存在很大的差异（图4）。京津唐地区细菌性痢疾发病数居前6位的分别是：农民（发病数：10 024例）、散居儿童（7650例）、离退人员（3264例）、学生（3235例）、家务及待业者（3205例）和干部职员（2806例）,占该地区本年全部发病病例的82.75%,其发病率分别为：23.93%、18.26%、7.79%、7.72%、7.65%和6.7%。图5分析了在年龄结构和性别上,细菌性痢疾发病的差异性。以5岁为一个年龄组[22-23],细菌性痢疾主要发生于儿童和老年期,这与中国其他地区细菌性痢疾的发生一致。另外,在各年龄组中,男性的发病率普遍高于女性发病率。
Fig. 4 The morbidity of bacillary dysentery ofdifferent professions

图4 不同职业细菌性痢疾的发病率

Fig. 5 The distribution of population and bacillary dysentery morbidity of male and female, respectively, at each age group

图5 各年龄组男性和女性的人口数和细菌性痢疾发病率在各年龄组的分布

注：各年龄段男(女)性的细菌性痢疾发病率=该年龄段男(女)细菌性痢疾发病数/该年龄段男(女)的人口数

3.2 时空聚集性

在京津唐地区,细菌性痢疾发病率在空间和时间上都存在很大的差异。空间上,细菌性痢疾发病率最低的地区是遵化市（2.89/10万）,最高为北京市门头沟区(115.77/10万);时间上,在12个月中,发病率最高的是8月：10.29/10万,最低的是2月：1.84/10万。热点分析（Getis_Ord Gi*）是用来探索和发现局部空间聚类分布特征的方法,标识出细菌性痢疾发病率空间聚集程度的高值和低值。使用空间热点分析工具Getis_Ord Gi*,分析京津唐地区各月细菌性痢疾发病率在空间和时间上是否存在聚集性及高低聚集区（图6）。空间上,1-4月,细菌性痢疾发病率的高聚集区主要分布于北京市的房山区和门头沟区,低聚集区域主要分布于唐山市的滦县;6-12月,细菌性痢疾发病率的高聚集区主要分布在天津市的滨海新区,无低聚集区。时间上,京津唐地区既存在细菌性痢疾的高聚集区又存在低聚集区,其中细菌性痢疾发病率的高聚集区在全年12个月均有分布,细菌性痢疾发病率的低聚集区只存在于1-4月以及6月。
Fig. 6 The monthly hotspot analysis of bacillary dysentery morbidity in Beijing-Tianjing-Tanshan

图6 京津唐地区细菌性痢疾发病率的热点分析

注：90%、95%分别代表置信度为90%、95%的统计显著性

3.3 社会经济因素

本文使用地理探测器中的因子探测器分析了影响因素对细菌性痢疾发病的影响强度即解释力,各影响因素的解释力依次为：农村人口占总人口的比例(q：0.61)>人口密度(q：0.37)>第一产业比重（q：0.23）>第二产业比重（q：0.22）>人均GDP（q：0.20）（图7）。结合皮尔逊相关性分析,结果显示人口密度与细菌性痢疾的发病率呈正相关(P<0.01);农村人口占总人口的比例、人均GDP、第一产业比重、第二产业比重与细菌性痢疾的发病率呈负相关 (P<0.01)。
Fig. 7 The power of determinant of risk factors to the incidence of bacillary dysentery

图7 各影响因子对细菌性痢疾发病的解释力

生态探测器分析了各影响因素的解释力qC,B值之间的差异性（表1）,并进行了显著性检验。可以看出,第一产业比重、第二产业比重之间的qC,B值没有显著的差异,但人口密度、农村人口占总人口的比例、人均GDP之间的qC,B值存在显著的差异。结合因子探测器的结果,可以判断出农村人口占总人口的比例、人口密度和各区县的人均GDP是细菌性痢疾发病的主要影响因素。
Tab. 1 Statistical significance difference of the q value between different risk factors

表1 不同影响因素的q值之间的统计显著性差异

人口密度 第一产业比重 第二产业比重 农村人口占总人口的比例 各区县的人均GDP
第一产业比重 N
第二产业比重 N N
农村人口占总人口的比例 Y Y Y
各区县人均GDP Y Y N Y

注：Y表示2个影响因素的解释力之间存在显著性差异（置信度为95%）;N表示没有显著性差异

交互作用探测器分析了2个影响因素对细菌性痢疾的交互作用。2个影响因素对细菌性痢疾的影响或相互独立,或相互影响,即2个影响因素对细菌性痢疾的共同作用大于或小于或等于这2个因素各自对细菌性痢疾的影响之和。结果显示：本研究所选的指标中,以人口密度和农村人口占总人口的比例2个指标为例,它们各自对细菌性痢疾的解释力为37%和61%,通过交互作用分析,二者共同对细菌性痢疾的解释力达到87%,即人口密度和农村人口占总人口的比例共同作用的结果要高于它们独自起作用的结果。通过表2数据分析,所选的指标之间都存在交互作用加强的趋势。
Tab. 2 The results of interaction detector

表2 交互作用探测器结果

人口
密度		 第一产
业比重		 第二产
业比重		 农村人口占总人口的比例 各区县的
人均GDP
人口密度 0.37
第一产业比重 0.79 0.23
第二产业比重 0.68 0.52 0.22
农村人口占总
人口的比例		 0.87 0.85 0.87 0.61
各区县的
人均GDP		 0.59 0.83 0.56 0.84 0.20

4 结论与展望

京津唐地区细菌性痢疾主要发生在夏秋季,这和中国其他地区细菌性痢疾发病的季节性一致,如长沙市、四川省、武汉市有相同的季节特征[11,24-25],并且该地区细菌性痢疾发病是从1月开始逐月增加,在8月达到顶峰,随后开始逐渐减少,这和同位于北部的城市沈阳市[14]的细菌性痢疾的研究结果一致。本研究区和其他区域的不同点在于季节高峰期的出现,京津唐地区的季节高峰在6-9月,比长沙市的提前了1个月[13]。对于人类传染病季节性的产生原因,流行病学家及相关研究者一直在研究中,并且对于传染病季节性的解释目前还没有统一的定论[26-27]。目前大多数研究中,对于季节性的解释主要归因于气象因素,如较高的温度可能会增加病原菌的爆发,增强细菌的生长,扩大细菌的生存环境进而污染食物[28]。另有研究表明,至贺氏杆菌生存的最适宜温度是37 ℃[29],而这样的高温天气主要出现在夏秋季节。
不同的职业类型、不同的年龄段,细菌性痢疾的发病率有很大差异,这主要和细菌性痢疾的传播途径、人体对细菌性痢疾的免疫有关。细菌性痢疾主要是通过人与人之间的接触以及粪—口途径传播,不同的年龄段和职业类型,人们之间的接触和周围的卫生环境都有很大的差异;儿童和老人的免疫力相比其他年龄组的较弱。并且不同菌群间及不同血清型痢疾杆菌之间没有交叉免疫,这也增加了细菌性痢疾的发病风险。
细菌性痢疾发病率的热点分析结果显示细菌性痢疾发病率的局部空间聚集状态保持一定的稳定性：1-5月,细菌性痢疾的高值聚集区主要是北京市的房山区和门头沟区,低聚集区主要是唐山市的滦县;6-12月,高值聚集区主要是天津市的滨海新区;同时揭示了细菌性痢疾空间分布的异质性。
本研究运用地理探测器模型,结合皮尔逊相关系数,研究了5种社会经济因素与细菌性痢疾的关系,探测了各因素对细菌性痢疾发生的影响程度。所选的5种社会经济指标,对细菌性痢疾都存在不同程度的影响,这和目前的研究发现一致,如Fereer等研究表明经济因素是细菌性痢疾发病的决定性因素之一[30]。本文的亮点在于探测了各影响因素对细菌性痢疾的解释力,并分析了影响因素两两之间对细菌性痢疾影响的交互作用。农村人口占总人口的比例、人口密度和各区县的人均GDP是影响细菌性痢疾发病的主要的社会经济因素,并且它们两两之间的交互作用要大于它们独自影响的作用。其中各区县的人均GDP与细菌性痢疾的发病率呈负相关。人均GDP作为衡量经济发展状况的指标,人均GDP越高,说明该地经济发展水平越高,经济发展有利于促进卫生的改善,更好地食用安全的水和食物。Tang等指出人们的收入越高,细菌性痢疾的发病率越小[31]。人口密度与细菌性痢疾的发病率呈正相关,农村人口占总人口的比例与细菌性痢疾的发病率呈负相关。随着经济和城镇化快速的发展,为了就业和生活的需要,农村人口逐渐向城镇和大城市转移[32]。京津唐地区作为京津冀发展的核心区域,经济和社会的快速发展吸引了来自全国各地外来人口的聚集,外来人口的迁入在为城市的发展提供大量人力资源的同时,也带来了人口过密、环境污染等诸多问题[33];并且外来工作者工作和活动范围的不确定性,这在很大程度上促进了细菌性痢疾的发生。其次,细菌性痢疾的预防和治疗对医疗技术要求并不是很高,一般地区均可满足,所以在农村人口占总人口的比例较高、人口密度低的郊区,其细菌性痢疾的发病率并不高,而在农村人口占总人口比例较低,人口密度高的地区,细菌性痢疾的发病率相对较高。地理探测器模型基于空间分异理论和空间方差分析,在探测出风险因素的同时,探测了各因素之间的相对重要性及其交互作用;并且地理探测器模型没有过多的条件限制和参数设置,普适性更好。
今后将进一步开展以下研究：①京津唐地区细菌性痢疾社会经济和气象因素之间的交互作用。虽然许多研究中已经证明气象因素对细菌性痢疾的影响及其它们之间的相关性,但本文只考虑了京津唐地区社会经济因素及其之间的交互作用,而气象因素和社会经济因素是否存在相互影响,它们之间的交互作用是加强还是减弱,还需要进一步的研究。②空间和时间上,将采用更小的研究单元（如空间上：镇乡;时间上：周）。由于社会经济数据统计口径等原因,本研究中以区县为空间分析单元,而在流行病学研究中,较小的地理单元在帮助提出一些更详尽的策略中可提供更有价值的信息,并且区县的社会经济水平并不能完全说明比其小的各地理单元（如镇乡等）的经济水平,所以在为镇乡等制定细菌性痢疾预防和控制措施时,使用区县级的研究结果并不准确,所以更小的地理单元（比如镇乡）将放入下一步的研究中。同样,今后的研究将以周为时间尺度分析细菌性痢疾发病率的聚集性。

The authors have declared that no competing interests exist.

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
Zhang Y, Peng B, Hiller J E .Weather and the transmission of bacillary dysentery in Jinan, northern China: a time-series analysis[J]. Public Health Rep, 2008,123:61-66.OBJECTIVES: This article aims to quantify the relationship between weather variations and bacillary dysentery in Jinan, a city in northern China with a temperate climate, to reach a better understanding of the effect of weather variations on enteric infections. METHODS: The weather variables and number of cases of bacillary dysentery during the period 1987-2000 has been studied on a monthly basis. The Spearman correlation between each weather variable and dysentery cases was conducted. Seasonal autoregressive integrated moving average (SARIMA) models were used to perform the regression analyses. RESULTS: Maximum temperature (one-month lag), minimum temperature (one-month lag), rainfall (one-month lag), relative humidity (without lag), and air pressure (one-month lag) were all significantly correlated with the number of dysentery cases in Jinan. After controlling for the seasonality, lag time, and long-term trend, the SARIMA model suggested that a 1 degree C rise in maximum temperature might relate to more than 10% (95% confidence interval 10.19, 12.69) increase in the cases of bacillary dysentery in this city. CONCLUSIONS: Weather variations have already affected the transmission of bacillary dysentery in China. Temperatures could be used as a predictor of the number of dysentery cases in a temperate city in northern China. Public health interventions should be undertaken at this stage to adapt and mitigate the possible consequences of climate change in the future.

DOI PMID

[2]
Von S L, Kim D R, Ali M, et al. A multicentre study of shigella diarrhoea in six Asian countries: disease burden, clinical manifestations, and microbiology[J]. PloS Med, 2006,3(9):e353.BACKGROUND: The burden of shigellosis is greatest in resource-poor countries. Although this diarrheal disease has been thought to cause considerable morbidity and mortality in excess of 1,000,000 deaths globally per year, little recent data are available to guide intervention strategies in Asia. We conducted a prospective, population-based study in six Asian countries to gain a better understanding of the current disease burden, clinical manifestations, and microbiology of shigellosis in Asia. METHODS AND FINDINGS: Over 600,000 persons of all ages residing in Bangladesh, China, Pakistan, Indonesia, Vietnam, and Thailand were included in the surveillance. Shigella was isolated from 2,927 (5%) of 56,958 diarrhoea episodes detected between 2000 and 2004. The overall incidence of treated shigellosis was 2.1 episodes per 1,000 residents per year in all ages and 13.2/1,000/y in children under 60 months old. Shigellosis incidence increased after age 40 years. S. flexneri was the most frequently isolated Shigella species (1,976/2,927 [68%]) in all sites except in Thailand, where S. sonnei was most frequently detected (124/146 [85%]). S. flexneri serotypes were highly heterogeneous in their distribution from site to site, and even from year to year. PCR detected ipaH, the gene encoding invasion plasmid antigen H in 33% of a sample of culture-negative stool specimens. The majority of S. flexneri isolates in each site were resistant to amoxicillin and cotrimoxazole. Ciprofloxacin-resistant S. flexneri isolates were identified in China (18/305 [6%]), Pakistan (8/242 [3%]), and Vietnam (5/282 [2%]). CONCLUSIONS: Shigella appears to be more ubiquitous in Asian impoverished populations than previously thought, and antibiotic-resistant strains of different species and serotypes have emerged. Focusing on prevention of shigellosis could exert an immediate benefit first by substantially reducing the overall diarrhoea burden in the region and second by preventing the spread of panresistant Shigella strains. The heterogeneous distribution of Shigella species and serotypes suggest that multivalent or cross-protective Shigella vaccines will be needed to prevent shigellosis in Asia.

DOI PMID

[3]
Wang X Y, Du L, Seidlein L V, et al. Occurrence of shigellosis in the young and elderly in rural China: results of a 12-month population-based surveillance study[J]. Am J Trop Med Hyg, 2005,73:416-422.In 2002, population- and treatment center-based surveillance was used to study the disease burden of in rural Hebei Province in the People's Republic of China. A total of 10,105 children with diarrhea or were enrolled. Infants were treated most frequently for diarrhea (1,388/1,000/year) followed by children < or = 5 years old (618/1,000/year). was treated most often in children 3-4 years old (32/1,000/year) and people > 60 years of age (7/1,000/year). Fifty-six percent (184 of 331) isolates were detected in patients who had non-. was identified in 93% of 306 isolates. The most common serotypes were 1a (34%), X (33%), and 2a (28%). More than 90% of the isolates were resistant to cotrimoxazole and nalidixic acid, but remained susceptible to ciprofloxacin, norfloxacin, and gentamicin. Widespread resistance to antibiotics adds urgency to the and use of vaccines to control .

DOI PMID

[4]
Wang X Y, Tao F B, Xiao D L, et al. Trend and disease burden of bacillary dysentery in China (1991-2000)[J]. Bull World Health Organ, 2006,84:561-568.

[5]
Sui J L, Zhang J, Sun J L, et al. Surveillance of bacillary dysentery in China, 2009[J]. Disease Surveillance, 2010,25:947-950.lt;strong>Objective </strong>To analyze the epidemic pattern of bacillary dysentery and the problems in surveillance in China by using the surveillance data obtained from national disease reporting information system.<br /><strong>Methods </strong>Descriptive epidemiological analysis was conducted.<br /><strong>Results </strong>The morbidity and mortality of bacillary dysentery declined in China in 2009. The cases were mainly children outside child care settings and farmers, and the incidence peak was during June-August. The overall detection rate of <em>Shigella</em> in 7764 samples collected in 20 surveillance sites in China was 8.73%% (678/7764). The pathogen type surveillance indicated that <em>S. flexneri</em> accounted for 67.26% (456/678) and <em>S. Sonnei</em> accounted for 32.74% (222/678). The drug-resistance of <em>Shegella</em> strains was common, but they were sensitive to third-generation cephalosporin, gentamicin and ciprofloxacin.<br /><strong>Conclusion </strong>National disease reporting information system and active surveillance in national surveillance sites of bacillary dysentery can provide more accurate information about the disease situation, which could facilitate the prevention and control of bacillary dysentery.

DOI

[6]
Nie C J, Li H R, Yang L S, et al. Socio-economic factors of bacillary dysentery based on spatial correlation analysis in Guangxi province, China[J]. PLoS ONE, 2014,9(7):e102020.Background In the past decade, bacillary dysentery was still a big public health problem in China, especially in Guangxi Province, where thousands of severe diarrhea cases occur every year. Methods Reported bacillary dysentery cases in Guangxi Province were obtained from local Centers for Diseases Prevention and Control. The 14 socio-economic indexes were selected as potential explanatory variables for the study. The spatial correlation analysis was used to explore the associations between the selected factors and bacillary dysentery incidence at county level, which was based on the software of ArcGIS10.2 and GeoDA 0.9.5i. Results The proportion of primary industry, the proportion of younger than 5-year-old children in total population, the number of hospitals per thousand persons and the rates of bacillary dysentery incidence show statistically significant positive correlation. But the proportion of secondary industry, per capital GDP, per capital government revenue, rural population proportion, popularization rate of tap water in rural area, access rate to the sanitation toilets in rural, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons and the rate of bacillary dysentery incidence show statistically significant negative correlation. The socio-economic factors can be divided into four aspects, including economic development, health development, medical development and human own condition. The four aspects were not isolated from each other, but interacted with each other.

DOI PMID

[7]
常昭瑞,孙强正,裴迎新,等. 2012年中国大陆地区细菌性痢疾疫情特点与监测结果分析[J].疾病监测,2014(7):528-532.

[ Chang Z R, Sun Q Z, Pei Y X, et al. Bacterial dysentery epidemic characteristics and monitoring result analysis in mainland China in 2012[J]. Disease Surveillance, 2014,7:528-532. ]

[8]
宋瑶. 天津市红桥区2012年细菌性痢疾流行病学特征分析[J].实用心脑肺血管病杂志,2014(3):38-39.目的分析天津市红桥区2012 年细菌性痢疾流行病学特征,掌握其流行规律。方法采用描述性流行病学方法,对天津市红桥区2012年细菌性痢疾患者病历资料进行分析。结果共报道细菌性痢 疾891例,年均发病率为167.63/10万,较去年同期下降了12.57%,无死亡病例;全年各月均有发病,8月是发病高峰期;红桥区各街道均有病例 发生,主要以丁字沽街、双环村街、咸阳北路街发病较多;散居儿童发病人数最多;0~1岁年龄组发病率最高,40~45岁年龄组发病率最低,其中男∶女为 1.12∶1。结论红桥区2012年细菌性痢疾的发病较去年同期有所下降,低年龄散居儿童为高危人群,发病有明显的季节性,患者多源自于红桥区流动人口密 集的街道。

DOI

[ Song Y.Bacterial dysentery epidemiological characteristics analysis in 2012, Hongqiao district of Tianjin[J].Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease,2014,3:38-39.]

[9]
高芳旭,齐秀英.天津市和平区细菌性痢疾流行特征及疫情预测[J].现代预防医学,2015(11):1951-1953.目的 了解2004-2013年菌痢流行特征,预测2014年10月-2015年9月菌痢月发病率.方法 采用描述性流行病学方法对2004-2013年天津市和平区菌痢发病情况进行分析,利用自回归求和移动平均(ARIMA)模型对2014年10月 -2015年9月菌痢发病率进行预测分析.结果 2004-2013年和平区共报告菌痢2 242例,平均年发病率为66.33/10万,男性发病率高于女性,0～9岁儿童发病率最高;发病主要集中在7-8月;白楼街平均发病率高于其他各街道. 病例以学生、干部职员和离退修人员为主.构建ARIMA(1,1,0)(1,1,0)模型的预测结果显示,2014年10月-2015年9月菌痢发病稳定 地处于较低水平,7月菌痢发病达高峰,月发病率为3.27/10万,流行趋势图与近10年的一致.结论 和平区菌痢疫情呈逐渐下降趋势,近年来已处于较低水平稳定状态,发病有明显季节性,儿童为高发人群,ARIMA(1,1,0)(1,1,0)模型在菌痢发 病率预测中显示了较好的精度.

[ Gao F X, Qi X Y.Bacterial dysentery epidemic characteristics and epidemic prediction, Helping district of Tianjin[J]. Modern Preventive Medicine, 2015,11:1951-1953. ]

[10]
郭宁,黄伟.天津市津南区2005-2011年细菌性痢疾流行病学特征分析[J].继续医学教育,2015(9):72-73.目的：分析2005～2011年天津市津南区细菌性痢疾的流行病学特征，为科学防治提供数据支持。方法通过中国疾病预防控制信息系统和日常监测中获取疾病发病资料数据，应用描述流行病学的方法进行统计分析。结果2005～2011年共报告菌痢2419例，发病率最高的年份为2008年，为97.45/10万，最低的年份为2011年，为45.69/10万，7～9月份为发病高峰季节，发病最多的为0～1岁组人群。结论菌痢仍是影响公众健康的一种重要传染病，但误诊率较高，应提高病原学诊断比例。

DOI

[ Guo N, Huang W.Bacillary dysentery epidemiological characteristics analysis in 2005-2011, Jinnan district of Tianjin[J]. Continuing Medical Education, 2015,9:72-73. ]

[11]
高雯,高庆华,何金奎,等.唐山市2005-2011年细菌性痢疾的流行病学分析[J].医学动物防制,2013(2):165-167.目的探讨福建省2005-2011年细菌性痢疾的流行特征,为制定预防控制策略提供科学依据。方法对福建省卫生机构2005-2011年通过全国疾病监测系统报告的细菌性痢疾数据进行描述性分析。结果 2005-2011年发病率呈下降趋势(χt2rend=703.98,P0.05)。7～9月为细菌性痢疾高发期,0～岁组发病率最高。男性发病率(6.96/10万)显著高于女性(4.95/10万),χ2=427.08,P0.05。乡村发病率(6.35/10万)高于城镇(5.61/10万),差异有统计学意义(χ2=57.48,P0.05)。发病率前3位地区分别为厦门、龙岩和漳州。散居儿童是最主要的发病人群,构成比为35.68%。最高诊断以临床诊断为主,其次为实验室诊断。结论一些地区发病率仍然较高,乡村较城镇的发病情况严重,5岁以下儿童是细菌性痢疾高发人群,有关部门应根据流行特征采取有效的预防控制措施。

[ Gao W, Gao Q H, He J K, et al. Epidemiological analysis of bacillary dysentery in Tangshan city in 2005-2011,China[J]. Chinese Journal of Pest Control, 2013,2:165-167. ]

[12]
Zhang Y, Bi P, Hiller J E, et al. Climate variations and bacillary dysentery in northern and southern cities of China[J]. Journal of Infection, 2007,55:194-200.lt;h2 class="secHeading" id="section_abstract">Summary</h2><h4 id="absSec_N29a8ed30N29d55690">Objectives</h4><p id="simple-para0070">This paper was aimed at examining the relationship between meteorological variables and bacillary dysentery in different climatic and geographic areas in China.</p><h4 id="absSec_N29a8ed30N29d556f0">Methods</h4><p id="simple-para0075">Jinan in northern China, with a temperate climate, and Baoan in southern China, with a subtropical climate were chosen as study areas. Spearman correlations and seasonal Autoregressive Integrated Moving Average (SARIMA) models were used to quantify the association between meteorological variables and dysentery. The Hockey Stick model was used to explore the threshold of the effect of temperatures.</p><h4 id="absSec_N29a8ed30N29d55750">Results</h4><p id="simple-para0080">Maximum temperature, minimum temperature, rainfall, relative humidity and air pressure were significantly correlated with the incidence of dysentery in the both cities, with lag effects varying from zero to two months. In the SARIMA models, maximum and minimum temperatures were significantly associated with dysentery transmission. The thresholds for the effects of maximum and minimum temperatures were 17&#xA0;&deg;C and 8&#xA0;&deg;C, respectively, in the northern city. No thresholds were detected in the southern city.</p><h4 id="absSec_N29a8ed30N29d557b0">Conclusions</h4><p id="simple-para0085">Climate variations have different impacts on the transmission of bacillary dysentery in temperate and subtropical cities in China. Public health action should be taken at this stage to reduce future risks of climate change with consideration of local climatic conditions.

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[13]
Gao L, Zhang Y, Ding G, et al. Meteorological variables and bacillary dysentery cases in Changsha city, China[J].The American Journal of Tropical Medicine and Hygiene, 2014,90:697-704.This study aimed to investigate the association between meteorological-related risk factors and bacillary dysentery in a subtropical inland Chinese area: Changsha City. The cross-correlation analysis and the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model were used to quantify the relationship between meteorological factors and the incidence of bacillary dysentery. Monthly mean temperature, mean relative humidity, mean air pressure, mean maximum temperature, and mean minimum temperature were significantly correlated with the number of bacillary dysentery cases with a 1-month lagged effect. The ARIMAX models suggested that a 1掳C rise in mean temperature, mean maximum temperature, and mean minimum temperature might lead to 14.8%, 12.9%, and 15.5% increases in the incidence of bacillary dysentery disease, respectively. Temperature could be used as a forecast factor for the increase of bacillary dysentery in Changsha. More public health actions should be taken to prevent the increase of bacillary dysentery disease with consideration of local climate conditions, especially temperature.

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[14]
Huang D S, Guan P, Guo J Q, et al. Investigating the effects of climate variations on bacillary dysentery incidence in northeast China using ridge regression and hierarchical cluster analysis[J]. BMC Infectious Diseases, 2008,8:130.Meteorological factors have affected the transmission of bacillary dysentery in northeast China. Bacillary dysentery prevention and control would benefit from by giving more consideration to local climate variations.

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[15]
Xiao G X, Xu C D, Wang J F, et al. Spatial-temporal pattern and risk factor analysis of bacillary dysentery in the Beijing-Tianjin-Tangshan urban region of China[J]. BMC Public Health, 2014,14:998.Bacillary dysentery remains a major public health concern in China. The Beijing–Tianjin–Tangshan urban region is one of the most heavily infected areas in the country. This study aimed to analyze epidemiological features of bacillary dysentery, detect spatial-temporal clusters of the disease, and analyze risk factors that may affect bacillary dysentery incidence in the region. Bacillary dysentery case data from January 2011 to December 2011 in Beijing–Tianjin–Tangshan were used in this study. The epidemiological features of cases were characterized, then scan statistics were performed to detect spatial temporal clusters of bacillary dysentery. A spatial panel model was used to identify potential risk factors. There were a total of 28,765 cases of bacillary dysentery in 2011. The results of the analysis indicated that compared with other age groups, the highest incidence (473.75/105) occurred in individuals <502years of age. The incidence in males (530.57/105) was higher compared with females (409.06/105). On a temporal basis, incidence increased rapidly starting in April. Peak incidence occurred in August (571.10/105). Analysis of the spatial distribution model revealed that factors such as population density, temperature, precipitation, and sunshine hours were positively associated with incidence rate. Per capita gross domestic product was negatively associated with disease incidence. Meteorological and socio-economic factors have affected the transmission of bacillary dysentery in the urban Beijing–Tianjin–Tangshan region of China. The success of bacillary dysentery prevention and control department strategies would benefit from giving more consideration to climate variations and local socio-economic conditions.

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[16]
丁悦,蔡建明,任周鹏,等.基于地理探测器的国家级经济技术开发区经济增长率空间分异及影响因素[J].地理科学进展,2014,33(5):657-666.建设国家级经济技术开发区（经开区）是中国扩大对外开放和促进区域发展的重要政策。历经30年多发展，国家级经开区已遍布全国，其个体间的发展差异也由于不同的动力机制而日趋显著。认识和探讨国家级经开区经济增长率的空间分异及其核心影响因素，对因地制宜制定发展策略、引导开发区高效发展具有重要意义。运用变异系数和地理探测器方法，分析2010年国家级经开区经济增长率的空间分异，并探测了其核心影响因素。结果表明：① 总体上，国家级经开区经济增长率在东中西三大区差异显著，呈现出高低高的U型格局；② 个体上，国家级经开区经济增长率在三大区内部存在不同分异特征，其中西部分异度最大、东部次之、中部最小；③ 探测因子决定力显示，主导三大地区国家级经开区经济增长率的核心要素明显不同；在所选出的5大核心影响因素中，中西东三大区呈现出由开发区内在因子主导向城市和区域性外在因子主导的转变趋势；④ 国家级经开区经济增长率及核心影响因素在三大区间的分异特征，一定程度上反映了开发区生命周期阶段性的演变规律。由此建议：近期内，中西部国家级经开区仍应聚焦于改进其自身发展要素；而从长远看，城市性和区域性的外部因子对经开区的影响将变得日益重要，亦即是经开区的未来发展将越来越依赖于与其所在城市和区域的有效融合。

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[ Ding Y, Cai J M, Ren Z P, et al. Geographical detectors-based spatial variation and factors of economic growth rate in state-level economic technological development zone[J]. Progress in Geography,2014,33(5):657-666. ]

[17]
Hu Y, Wang J F, Li X H, et al. Geographical detector based risk assessment of the under- five mortality in the 2008 Wenchuan earthquake, China[J]. PLoS ONE, 2011,6(6):e21427.On 12 May, 2008, a devastating earthquake registering 8.0 on the Richter scale occurred in Sichuan Province, China, taking tens of thousands of lives and destroying the homes of millions of people. Many of the deceased were children, particular children less than five years old who were more vulnerable to such a huge disaster than the adult. In order to obtain information specifically relevant to further researches and future preventive measures, potential risk factors associated with earthquake-related child mortality need to be identified. We used four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) based on spatial variation analysis of some potential factors to assess their effects on the under-five mortality. It was found that three factors are responsible for child mortality: earthquake intensity, collapsed house, and slope. The study, despite some limitations, has important implications for both researchers and policy makers.

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[18]
廖颖,王心源,周俊明. 基于地理探测器的大熊猫生境适宜度评价模型及验证[J].地球信息科学学报,2016,18(6):767-778.动物生境适宜度评价对于野生动物生境保护十分重要。基于物种活动点来建模的生态位模型是目前应用最广泛的动物生境评价方法,但该方法不能直接表达生境适宜度与环境因子间具有生态学意义的数量关系。本文以雅安地区为例,提出一种新的大熊猫（Ailuropoda melanoleuca）生境适宜度评价方法,选取海拔、坡度、坡向、地形指数、距水源距离、植被类型、主食竹及距公路距离8个环境因子,引入地理探测器,在分别基于MAXENT模型和层次分析法（The Analytic Hierarchy Process,AHP）所构建生境适宜度模型的基础上,通过4个地理探测器（风险探测器、因子探测器、生态探测器和交互作用探测器）探寻大熊猫生境与各环境因子间的关系以及环境因子对大熊猫生境的影响机理,并将其预测结果与单一MAXENT模型和AHP法进行对比。结果表明：（1）AHP、AHP-Geogdetector、MAXENT和MAXENT-Geogdetector模型总体评价精度分别为85.6%、86.5%、91.3%和94.2%,kappa系数分别为0.699、0.718、0.821和0.882,AUC值分别为0.902、0.928、0.949和0.966,模型所预测的适宜和较适宜区与实际分布区重叠比分别为63.66%、61.30%、76.70%和90.10%,说明AHP-Geogdetector和MAXENT-Geogdetector模型精度均比相应的单一模型有所提高,且MAXENT-Geogdetector模型精度最高;（2）基于地理探测器的大熊猫生境适宜度评价模型能以“生境适宜度和环境因子间具有生态学意义的数量关系”的形式直接体现环境因子对动物生境利用的生态学作用,具有较好的生态学可解释性。因此,用地理探测器进行大熊猫生境适宜度评价具有较好的可行性。

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[ Liao Y, Wang X Y, Zhou J M, Suitability assessment and validation of giant panda habitat based on geographical detector[J]. Journal of Geoinformation Science, 2016,18(6):767-778. ]

[19]
Wang J F, Li X H, Christakos G, et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China[J]. International Journal of Geographical Information Science, 2010,24(1):107-127.Physical environment, man‐made pollution, nutrition and their mutual interactions can be major causes of human diseases. These disease determinants have distinct spatial distributions across geographical units, so that their adequate study involves the investigation of the associated geographical strata. We propose four geographical detectors based on spatial variation analysis of the geographical strata to assess the environmental risks of health: the risk detector indicates where the risk areas are; the factor detector identifies factors that are responsible for the risk; the ecological detector discloses relative importance between the factors; and the interaction detector reveals whether the risk factors interact or lead to disease independently. In a real‐world study, the primary physical environment (watershed, lithozone and soil) was found to strongly control the neural tube defects (NTD) occurrences in the Heshun region (China). Basic nutrition (food) was found to be more important than man‐made pollution (chemical fertilizer) in the control of the spatial NTD pattern. Ancient materials released from geological faults and subsequently spread along slopes dramatically increase the NTD risk. These findings constitute valuable input to disease intervention strategies in the region of interest.

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[20]
王劲峰,廖一兰,刘鑫.空间数据分析教程[M].北京:科学出版社,2010:50-51.

[ Wang J F, Liao Y L. Liu X.Spatial data analysis tutorial[M]. Beijing: Science Press, 2010:50-51. ]

[21]
Wang J F, Zhang T L, Fu B J.A measure of spatial stratified heterogeneity. Ecological Indicators, 2016, 67: 250-256.Spatial stratified heterogeneity, referring to the within-strata variance less than the between strata-variance, is ubiquitous in ecological phenomena, such as ecological zones and many ecological variables. Spatial stratified heterogeneity reflects the essence of nature, implies potential distinct mechanisms by strata, suggests possible determinants of the observed process, allows the representativeness of observations of the earth, and enforces the applicability of statistical inferences. In this paper, we propose a q-statistic method to measure the degree of spatial stratified heterogeneity and to test its significance. The q value is within [0,1] (0 if a spatial stratification of heterogeneity is not significant, and 1 if there is a perfect spatial stratification of heterogeneity). The exact probability density function is derived. The q-statistic is illustrated by two examples, wherein we assess the spatial stratified heterogeneities of a hand map and the distribution of the annual NDVI in China.

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[22]
张钟,程婷婷,马涛,等.南京市2005-2012年细菌性痢疾流行特征分析[J].中华疾病控制杂志,2014,11:1047-1050.目的 分析2005-2012年南京市细菌性痢疾的流行病学特征,为预防控制细菌性痢疾提供科学依据.方法 对南京市2005-2012年细菌性痢疾监测数据做回顾性分析.结果 2005-2012年南京市细菌性痢疾年均发病率为21.09/10万,发病率呈现逐年下降的趋势(x2趋势=2 496.57,P＜0.001).用圆形分布法可得南京市菌痢发病高峰日为8月4日,高峰期为5月20日～9月19日(Rayleigh Z=2 046.29,查表知P＜0.01).城区发病率高于郊区,男性发病率高于女性,0～岁组、20～岁组和≥70岁组发病率较高,病例的职业以散居儿童和学生居多.另外,南京市细菌性痢疾实验室诊断率较低,并呈现逐年下降的趋势(x2趋势=68.22,P＜0.001).结论 细菌性痢疾仍是南京市需重点防控的传染病之一.应加大对重点人群、重点地区的健康宣教力度,采取综合性防控措施有效控制细菌性痢疾的蔓延.

[ Zhang Z, Cheng T T, Ma T, et al. Epidemiological analysis of bacillary dysentery in Nanjing city in 2005-2012, China[J]. Chinese Journal of Disease Control & Prevention, 2014,11:1047-1050. ]

[23]
杨天池,毛国华,施家威. 2004-2011年浙江省宁波市细菌性痢疾流行病学分析[J].疾病监测,2012(8):620-622.

[ Yang T C, Mao G H, Shi J W.Epidemiological analysis of bacillary dysentery in Ningbo city of Zhejiang province in 2004-2011, China[J]. Disease Surveillance, 2012,8:620-622. ]

[24]
Ma Y, Zhang T, Liu L, et al. Spatio-temporal pattern and socio-economic factors of bacillary dysentery at county level in Sichuan province, China[R]. Scientific Reports, 2015.

[25]
Li Z J, Wang L G, Sun W G, et al. Identifying high-risk areas of bacillary, dysentery and associated meteorological factors in Wuhan, China[R]. Scientific Reports, 2013.

[26]
Fares A.Factors influencing the seasonal patterns of infectious diseases[J]. Int J Prev Med, 2013,4:128-132.The recognition of seasonal patterns in infectious disease occurrence dates back at least as far as the hippocratic era, but the mechanisms underlying these fluctuations remain poorly understood. Many classes of mechanistic hypotheses have been proposed to explain seasonality of various directly transmitted diseases, including at least the following; human activity, seasonal variability in human immune system function, seasonal variations in vitamin D levels, seasonality of melatonin, and pathogen infectivity. In this short paper will briefly discuss the role of these factors in the seasonal patterns of infectious diseases.

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[27]
Grassly N C & Fraser C. Seasonal infectious disease epidemiology[J]. Proceedings of the Royal Society of London B: Biological Sciences, 2006,273:2541-2550.Seasonal change in the incidence of is a common phenomenon in both temperate and tropical climates. However, the mechanisms responsible for seasonal disease incidence, and the epidemiological consequences of seasonality, are poorly understood with rare exception. Standard epidemiological theory and concepts such as the basic reproductive number R0 no longer apply, and the implications for interventions that themselves may be periodic, such as pulse vaccination, have not been formally examined. This paper examines the causes and consequences of seasonality, and in so doing derives several new results concerning vaccination strategy and the interpretation of disease outbreak data. It begins with a brief review of published scientific studies in support of different causes of seasonality in of , identifying four principal mechanisms and their association with different routes of transmission. It then describes the consequences of seasonality for R0, disease outbreaks, endemic dynamics and persistence. Finally, a mathematical analysis of routine and pulse vaccination programmes for seasonal is presented. The synthesis of seasonal epidemiology attempted by this paper highlights the need for further empirical and theoretical work.

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[28]
Checkley W, Epstein L D, Gilman R H, et al. Effects of EI Nino and ambient temperature on hospital admissions for diarrhoeal diseases in Peruvian children[J]. The Lancet, 2000,355:442-450.To investigate whether the El Ni09o phenomenon and ambient temperature had an effect on the epidemiology of childhood diarrhoea, we analysed data on daily number of admissions of children with diarrhoea to the Oral Rehydration Unit of the Instituto de Salud del Ni09o in Lima, Peru, between January, 1993, and November, 1998.We obtained daily data on hospital admissions from the Oral Rehydration Unit, and meteorological data from the Peruvian Weather Service, and used time-series linear regression models to assess the effects of the 1997-98 El Ni09o event on admissions for diarrhoea.57,331 children under 10 years old were admitted to the unit during the study. During the 1997-98 El Ni09o episode, mean ambient temperature in Lima increased up to 5 degrees C above normal, and the number of daily admissions for diarrhoea increased to 200% of the previous rate. 6225 excess admissions were attributable to El Ni09o, and these cost US$277,000. During the period before the El Ni09o episode, admissions for diarrhoea increased by 8% per 1 degree C increase in mean ambient temperature. The effects of El Ni09o and ambient temperature on the number of admissions for diarrhoea were greatest during the winter months.El Ni09o had an effect on hospital admissions greater than that explained by the regular seasonal variability in ambient temperature. The excess increase in ambient temperature was the main environmental variable affecting admissions. If our findings are reproducible in other regions, diarrhoeal diseases may increase by millions of cases worldwide with each degree of increase in ambient temperature above normal.

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[29]
Lake I R, Gillespie I A, Bentham G, et al. A re-evaluation of the impact of temperature and climate change on foodborne illness. Epidemiology and Infection, 2009,137:1538-1547.The effects of temperature on reported cases of a number of foodborne illnesses in England and Wales were investigated. We also explored whether the impact of temperature had changed over time. Food poisoning, campylobacteriosis, salmonellosis, Salmonella Typhimurium infections and Salmonella Enteritidis infections were positively associated (P<0.01) with temperature in the current and previous week. Only food poisoning, salmonellosis and S. Typhimurium infections were associated with temperature 2-5 weeks previously (P<0.01). There were significant reductions also in the impact of temperature on foodborne illnesses over time. This applies to temperature in the current and previous week for all illness types (P<0.01) except S. Enteritidis infection (P=0.079). Temperature 2-5 weeks previously diminished in importance for food poisoning and S. Typhimurium infection (P<0.001). The results are consistent with reduced pathogen concentrations in food and improved food hygiene over time. These adaptations to temperature imply that current estimates of how climate change may alter foodborne illness burden are overly pessimistic.

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[30]
Ferrer S R, Strina A, Jesus S R, et al. A hierarchical model for studying risk factors for childhood diarrhoea: a case-control study in a middleincome country[J]. Int J Epidemiol, 2008,37:805-815.OBJECTIVE: To identify factors associated with diarrhoea occurrence in children in a city in a middle-income country, with high access to water and sanitation. METHODS: A case-control study in the city of Salvador, north-eastern Brazil was conducted from November 2002 to August 2004. The study population consisted of children presenting at a health facility. A total of 1688 cases of diarrhoea and 1676 controls were selected. Data collection was by a questionnaire and structured observation during home visits. The explanatory variables were grouped according to a conceptual model defined previously. Analysis was done using a hierarchical approach, to provide a more dynamic view of the transmission characteristics of childhood diarrhoea. Non-conditional logistic regression was used, and odds ratio and population-attributable fractions were estimated. RESULTS: Socioeconomic factors contributed most to determining diarrhoea occurrence, followed by interpersonal contact, while factors related to food preparation, the environment and water and sanitation made a smaller contribution. CONCLUSION: The findings indicate that the transmission of diarrhoea is influenced by factors from all hierarchical levels, with interpersonal transmission playing a relatively higher role than previously thought. This is compatible with a predominance of viruses and other agents spread by interpersonal routes including Shigella, Giardia and Cryptosporidium. Diarrhoea control strategies in similar settings (middle-income countries in which a large proportion of the population has access to water and sanitation) must give greater emphasis to policies geared towards reducing person-to-person transmission for the prevention of diarrhoea.

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[31]
Tang F Y, Cheng Y J, Bao C J, et al. Spatio-Temporal Trends and Risk Factors for Shigella from 2001 to 2011 in Jiangsu Province, China[J]. PLoS One, 2014,9(1):e83487.This study aimed to describe the spatial and temporal trends of Shigella incidence rates in Jiangsu Province, People's Republic of China. It also intended to explore complex risk modes facilitating Shigella transmission.County-level incidence rates were obtained for analysis using geographic information system (GIS) tools. Trend surface and incidence maps were established to describe geographic distributions. Spatio-temporal cluster analysis and autocorrelation analysis were used for detecting clusters. Based on the number of monthly Shigella cases, an autoregressive integrated moving average (ARIMA) model successfully established a time series model. A spatial correlation analysis and a case-control study were conducted to identify risk factors contributing to Shigella transmissions.The far southwestern and northwestern areas of Jiangsu were the most infected. A cluster was detected in southwestern Jiangsu (LLR66=6611674.74, P<0.001). The time series model was established as ARIMA (1, 12, 0), which predicted well for cases from August to December, 2011. Highways and water sources potentially caused spatial variation in Shigella development in Jiangsu. The case-control study confirmed not washing hands before dinner (OR66=663.64) and not having access to a safe water source (OR66=662.04) as the main causes of Shigella in Jiangsu Province.Improvement of sanitation and hygiene should be strengthened in economically developed counties, while access to a safe water supply in impoverished areas should be increased at the same time.

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[32]
钮薇娜. 从县城城镇化水平看农村人口空间转移[J].农业经济丛刊,1987(1):29.正 根据各地不同的经济、地理、人文条件,我国一部分农村人口正向城镇进行空间转移。然而,在不同地区转移的规模和趋势是不同的。这里试通过县城城镇化水平给 予推算。目前,苏南农村经济发展模式在长江和珠江三角洲、胶东和辽东半岛、京津唐地区,广大沿海地区及大、中城市郊区推广较快。这类地区县城城镇化水平有 的目前已达到25%左右,如昆山县为25.3%,武汉市的新洲县为26%,北京市九个远郊县集镇人口比重为18.2～28.7

[ Niu W N.Analyze rural population spatial transfer from the level of urbanization in the county, Taiwanese Agricultural Economic Review,1987,1:29.]

[33]
王海宁,陈媛媛. 京津沪外来人口迁移行为影响因素对比分析[J].人口与发展,2010(2):21-28.以2008年京、津、沪、穗四大城市外来人口问卷调查资料为基础,对比分析了北京、天津和上海迁移人口的个体特征以及相对于其他省市,三座城市与迁出地在经济和社会发展差异上对外来人口迁移行为的影响,讨论了人口迁移对地区间不均衡程度的影响.无论是在个体特征方面,还是受经济和社会发展影响面,天津和上海的迁移人口都具有较多的趋同性;北京市由于其特殊的城市功能以及拥有众多优势资源,对高素质人才具有较强的吸引力,经济和社会的发展等因素对迁移者迁移概率的影响有所不同.

[ Wang H N, Chen Y Y.Comparative analysis migratory behavior’s factors of transient population in Beijing-Tianjin-Shanghai[J]. Population and Development, 2010,2:21-28. ]

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