基于空间点模式分析的疾病分布状态的量化指标研究

目的 从空间点模式分析的角度探讨疾病分布状态的量化指标,为疾病的预防控制提供新的统计学方法.方法 基于病例间的距离系统地总结了空间点模式分析中反映疾病分布状态的定量统计指标--G函数、F函数、J函数和K函数.在介绍其基本原理的基础上,研究距离取0到3000 m,间隔为50 m,使用4个指标对贵池区的急性血吸虫病病例资料进行分析,并在SaTScan软件中用空间移动扫描圆形窗口法进行分析验证.结果 贵池区6年共发生83例急性血吸虫病病例,空间点模式分布图显示病例主要分布在长江和秋浦河附近.获得了4个定量统计指标的计算方法,用其对上述急性血吸虫病资料分析发现病例分布具有空间聚集性(G函数和K函数位于95%可信区间的上方,F函数和J函数位于95%可信区间的下方),与空间移动扫描圆形窗口法的结果一致,后者并发现1个最可能的聚集区域,圆心坐标为(30.65 N,117.44 E),圆半径为2.69 km,相对危险度为12.78(对数似然比=32.80,P=0.0001).结论 量化统计指标的应用不仅解决了传统标点地图不能量化分析的缺点,而且为深入的空间聚集性分析提供了理论依据.     

疾病家庭聚集的β—二项分布模型

疾病家庭聚集的β－二项分布模型陈峰１陈佩珍１杨树勤２疾病家庭聚集的统计分布模型是流行病学、遗传学研究工作者一直关注的课题，弄清疾病在家庭内的理论分布，可为病因学研究、统计方法的选择等提供线索。一般认为如果疾病在家庭无聚集现象，则家庭内病例数的分布服从...     

空间扫描统计在广西肝癌空间格局中的应用研究

目的 探测广西肝癌发病率的空间分布规律,为肝癌防治提供科学依据.方法 应用空间扫描统计方法对广西肝癌病例在全区范围内的空间分布格局进行探测,并结合地理信息系统实现探测结果的可视化.结果 空间扫描统计表明广西肝癌高发区聚集在圆心为东经107.8152°、北纬22.5677°、半径为78.53km的区域内(LLR=997.661、RR=4.918、P=0.001);疾病专题图亦显示肝癌高发区聚集在桂西南.结论 空间扫描统计可用于探测疾病的高发聚集区,并对聚集区大小和位置进行准确定位,为广西肝癌防治提供科学依据.     

空间分析中空间自相关性的诊断

目的 大部分流行病学资料具有空间属性,即空间相关性和空间异质性,本研究拟从空间自相关统计量、变异函数图、空间自回归模型等3方面探讨具有空间属性资料的空间自相关性分析方法.方法 分别阐述空间自相关统计量、变异函数图和空间自回归模型的原理和计算方法,对其各自的特点进行比较,并以2009年广东省手足口病的发病为例阐述软件实现的过程以及对参数和统计量的解释.结果 基于广义Pearson相关系数的空间自相关统计量可以定量地分析疾病在空间上的聚集状态以及相关程度;变异函数可以对疾病发生的空间变异进行分解,并解释由空间自相关部分引起的变异大小;空间自回归模型则可以扣除间间自相关性的影响后分析影响因素的作用大小.结论 空间自相关统计量、变异函数图、空间自回归模型是定性和定量地分析空间自相关性的方法,三者结合可以很充分地分析疾病的空间自相关性.     

地理抽样单元面积对疾病空间分布量化指标的影响

[目的]探讨地理抽样单元面积大小对疾病空间分布概率模型及其量化指标的影响。[方法]采用模型模拟的指标模拟方法，观察地理抽样单元面积由小到大变化时，概率模型类型及其参数和量化统计指标是否变化。[结果]随着地理抽样单元面积的变化，概率模型类型及其参数和量化统计指标也随之变化。[结论]疾病空间分布的概率模型及其量化统计指标是空间尺度的函数，具有空间尺度依赖性，应用时必须根据研究目的事先确定空间尺度；疾病空间分布的概率模型及其量化统计指标存在空间尺度依赖的缺陷，应建立不依赖空间尺度的疾病空间相关-自相关模型。     

地理抽样单元面积对疾病空间分布量化指标的影响

[目的]探讨地理抽样单元面积大小对疾病空间分布概率模型及其量化指标的影响.[方法]采用模型模拟和指标模拟方法,观察地理抽样单元面积由小到大变化时,概率模型类型及其参数和量化统计指标是否变化.[结果]随着地理抽样单元面积的变化,概率模型类型及其参数和量化统计指标也随之变化.[结论]疾病空间分布的概率模型及其量化统计指标是空间尺度的函数,具有空间尺度依赖性,应用时必须根据研究目的事先确定空间尺度;疾病空间分布的概率模型及其量化统计指标存在空间尺度依赖性的缺陷,应建立不依赖空间尺度的疾病空间相关-自相关模型.     

上海金山区中小学生呼吸系统病因缺课空间分析

探究上海市金山区中小学生呼吸系统病因缺课率的空间分布特征,为降低呼吸系统病因缺课率提供理论依据.方法 以街镇为单位,采用空间统计方法对金山区2013-2017学年41 089名因呼吸系统症状/疾病缺课中小学生的信息,结合地理信息进行空间格局分布及空间聚集性分析.结果 中小学生2013-2017学年因呼吸系统症状/疾病缺课率为0.80‰.5学年各街镇呼吸系统病因缺课率分布格局变化较小,局部空间自相关分析存在1个高-高聚集区域.时空扫描统计量分析发现存在1个主要聚集区域(LLR=2 382.9,RR=2.89,P<0.01),分布在金山区东南方向街镇;2个次要聚集区域(LLR值分别为34.6,14.9,RR值分别为1.19,1.13,P值均<0.01)分布在金山西北方向和南部街镇.结论 上海金山区中小学生呼吸系统病因缺课具有时空聚集性,应加强金山区东南方向呼吸系统病因防控工作.     

上海市2006-2016年戊型病毒性肝炎的时空分布特征分析

目的 分析2006－2016年上海市戊型病毒性肝炎（戊肝）空间聚集性和时空分布特征,为戊肝防控工作提供依据。方法 戊肝发病数据和医疗机构诊疗信息来源于中国疾病预防控制信息系统。地理信息数据来源于上海市测绘院提供,地图比例为1∶750 000。采用ArcGIS 10.1软件对戊肝发病数据的空间聚集性进行全局和局部自相关分析,采用SaTScan 9.4.4软件对戊肝发病数据的时空聚集性进行时空扫描分析。结果 2006－2016年上海市累计报告戊肝病例6 048例,年均发病率为2.14/10万。空间自相关分析显示,戊肝发病率存在空间正相关,“高-高”聚集区域主要位于上海市中心城区,发病率存在明显的时空聚集性特征。结论 掌握2006－2016年上海市戊肝发病数据的空间分布特点、时空聚集区域等时空分布特征,对于合理配置公共卫生资源、有效预防和控制戊肝,有重要的公共卫生学意义。     

扫描统计及其在流行病学中的应用

扫描统计(scan statistic)是空间统计学方法之一,其目的在于探测空间、时间、时空范围内某事件发生数的异常增加,并检验这种改变是否由于随机变异所造成.即探测研究区域内是否存在聚集性、聚集性的确切位置、聚集性的风险大小,并检验聚集性有无统计学意义.疾病的发生与流行常常表现为地域性病例数的增加与减少,地域性病例分布的聚集与消失.     

疾病的空间聚集性分析

在疾病的空间聚集性研究中，如果遇到人口分布情况不明，常使分析结果难以断论。排列检验（ｐｅｒｍｕｔａｔｉｏｎｔｅｓｔ）可作为一种在人口分布未知情况下的疾病空间聚集性检验。它以随机样本的分布作为理论分布，判断病例组的情况是否在理论分布的小概率区域内，从而得出有无疾病空间聚集性的结论。     

2006—2008年宁波市农村居民肝癌发病情况分析

目的：了解和分析宁波市农村居民肝癌发病情况和流行特点．为开展肝癌综合防治提供依据。方法：收集整理宁波市农村卫生监测区2006～2008年肝癌发病资料．统计分析其粗发病率和标化发病率、分性别年龄分布、最高诊断依据构成等。结果：2006～2008年宁波市农村监测区肝癌粗发病率、标化发病率分别为38．66／10万、32．14／10万。男女性年龄别发病率均随年龄增长而上升,男性发病率高于女性,性别比为2．77：1。最高诊断依据列首位的是影像学诊断·占58.93％;列第二位的是病理诊断,占36．72%。结论：宁波市农村居民肝癌发病率处于省内和全国的中上水平,病例的诊断和报告质量尚可．应进一步加强病因研究和防治工作。     

Patrón espacial de la legionelosis en España, 2003-2007

Objectives

To analyze the spatial pattern of legionellosis in Spain for men and women during the period 2003-2007 and to identify spatial clustering of risk.

Methods

We identified the spatial pattern of the distribution of legionellosis rates based on calculation of rates by municipality through the direct method. Smoothing of these rates was performed by the Empirical Bayes method for studying the spatial pattern of disease for both sexes. We used Morańs index to analyze spatial autocorrelation rates globally. To calculate local rates, the Local Moran's Index [known as local indicators of spatial association (LISA)], was used to analyze the clusters of municipalities with the highest risk.

Results

After smoothing the risk, the highest rates (over 50 per 100,000 inhabitants) were grouped in the eastern Mediterranean coastal areas and the north of the mainland, as well as in the Mediterranean islands. Moran's index smoothed rates were 0.15 for men and 0.23 for women. The spatial clusters of statistically significant higher rates calculated by the LISA index were distributed in the north and east for both sexes.

Conclusions

These methods of spatial analysis allow patterns of disease distribution to be identified. All the methods used yielded similar results. These techniques are a complementary tool for epidemiological surveillance of infectious diseases.     

三维自回归趋势面模型在疾病时空动态分析中的应用

目的 探讨三维自回归趋势面模型技术在疾病时空动态分析预测中的应用价值。方法 在空间相关－自相关假设下,提出二维自回归趋势面模型,通过加入时间变量ｔ,构造三维自回归趋势面模型。结果 获得三维自回归趋势面模型及其建模方法。结论 该模型不仅可分析疾病的空间趋势,耐用而且可分析其空间趋势的时间变化特征。     

Spatial modelling of disease using data- and knowledge-driven approaches

The purpose of spatial modelling in animal and public health is three-fold: describing existing spatial patterns of risk, attempting to understand the biological mechanisms that lead to disease occurrence and predicting what will happen in the medium to long-term future (temporal prediction) or in different geographical areas (spatial prediction). Traditional methods for temporal and spatial predictions include general and generalized linear models (GLM), generalized additive models (GAM) and Bayesian estimation methods. However, such models require both disease presence and absence data which are not always easy to obtain. Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability. Other methods, such as multicriteria decision analysis (MCDA), use knowledge of the causal factors of disease occurrence to identify areas potentially suitable for disease. In addition to their less restrictive data requirements, some of these novel methods have been shown to outperform traditional statistical methods in predictive ability (Elith et al., 2006). This review paper provides details of some of these novel methods for mapping disease distribution, highlights their advantages and limitations, and identifies studies which have used the methods to model various aspects of disease distribution.     

Index of endemicity

The author discusses the difficulties involved in defining the term “endemicity”, and suggests a new approach to the problem—namely, the establishment of indices of endemicity, based on such data as are usually collected by national health administrations (mortality and morbidity rates, spleen-rates, case incidence in seaports, etc.). Examples are given of the calculation of the endemicity index for a number of diseases from different types of data obtained from various countries. An important advantage of the endemicity index is that it provides an easy means of studying the geographical pattern of endemic foci of disease.     

2007-2017年广东省HIV/AIDS流行时空分布特征

目的 了解2007-2017年广东省艾滋病病毒感染者（human immunodeficiency virus,HIV）和艾滋病病人（acquired immunodeficiency syndrome,AIDS）的流行特征及其时空分布特征,为制定防治策略提供依据。方法 以2007-2017年广东省各县（区）历年报告的HIV/AIDS为基础,用OpenGeoDa 1.8软件进行全局空间自相关和局部空间自相关分析,SaTScan 9.4进行时空扫描分析,用Arcgis 10.4进行可视化展示。结果 广东省2007-2017年共新发现HIV/AIDS 76 476例,病例的空间分布范围逐年扩大并且局部地区分布集中。各年全局Moran's I 0.25~0.43之间（均有P<0.05）,说明2007-2017年存在全局空间自相关。时空扫描统计分析显示深圳市6个县（区）为一类聚集区域,阳江市、罗定市等6个县（区）为二类聚集区域,广州市、佛山等19个县（区）为三类聚集区域（均有P<0.05）。结论 艾滋病发病的高高聚集区域逐渐覆盖珠三角大部分县（区）,阳江市及周边地区逐步退出高高聚集区,提示珠三角区域为广东省近几年艾滋病防控重点区域。     

Analysis of the spatial distribution of cryptosporidiosis in AIDS patients in San Francisco using density equalizing map projections (DEMP)

Environmental transmission of cryptosporidiosis has occurred repeatedly in defined spatial areas during outbreaks of disease attributed, for example, to drinking water contamination. Little work has been done to investigate the possibility of cryptosporidiosis infection in defined spatial areas in non-outbreak (i.e., endemic) settings. This study applies a novel approach to the investigation of the spatial distribution of cryptosporidiosis in AIDS patients in San Francisco. Density equalizing map projection (DEMP) maps were created for nine race/ethnicity-age groups of AIDS patients based on census tract of residence. Additionally, census tracts with a “high density” of cryptosporidiosis cases were identified by applying smoothing techniques to the DEMP maps, and included as a covariate in multivariate Poisson regression analyses of other known risk factors for cryptosporidiosis. These analyses suggest: (1) cases of cryptosporidiosis among Black and Hispanic AIDS patients, but not among Whites, show a statistically significant non-random spatial distribution (p<0.05) even after adjustment for the underlying spatial distribution of AIDS patients for these demographic groups, and (2) the risk of residence in these high density census tracts, adjusted for other known risk factors, was not statistically significant (relative risk=1.27, 95% confidence interval 0.15, 10.53). These results do not support an independent effect of spatial distribution on the transmission of cryptosporidiosis among AIDS patients.     

A Novel Method for Detecting Association Between DNA Methylation and Diseases Using Spatial Information

DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.     

A method for quantifying artefacts in mapping methods illustrated by application to headbanging

Maps of disease rates (and other quantities) often must contend with variance associated with variable population sizes and low incidence within spatial units. These characteristics can lead to substantial statistical noise that can mask underlying spatial variation. As Gelman and Price illustrated, most conventional mapping methods fail to address this problem, and in fact can introduce statistical artefacts; mapped quantities can show spatial patterns even when there are no spatial patterns in the underlying parameter of interest. Kafadar evaluated the performance of the headbanging algorithm for spatial smoothing (Tukey and Tukey, Hansen) for eliminating small scale variation and preserving edge structure. Here we perform a simulation study to investigate the artefacts of maps smoothed by unweighted and weighted headbanging. We find substantial artefacts that depend on the spatial structure of the statistical variation (for example, the spatial pattern of sample sizes) and on the details of the spatial distribution of geographic units. The methods used here could readily be adapted to study other spatial smoothers; we choose headbanging because (i) it is an important method used in practice, and (ii) its heavily computational nature is naturally studied using simulation (in contrast to the analytical methods used by Gelman and Price).