出生缺陷的空间点格局分析

目的探讨新生儿神经管畸形的空间点格局分布规律。方法收集山西省和顺县1998—2001年新生儿神经管畸形病例数据,采用核密度估计和Ripley’sK函数进行空间点格局分析。结果核密度估计得出,神经管畸形在研究区域的中部和东南部呈聚类分布;Ripley’sK函数的分析得出,神经管畸形发病点在空间距离3.17～10.41km内聚类趋势显著。结论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,为进行区域环境与出生缺陷致病因子的空间分析识别提供了重要信息。     

Characterizing the spatial relationship between smoking status and tobacco retail exposure: Implications for policy development and evaluation

Tobacco retail density and smoking prevalence remain elevated in marginalized communities, underscoring the need for strategies to address these place-based disparities. The spatial variation of smokers and tobacco retailers is often measured by aggregating them to area-level units (e.g., census tracts), but spatial statistical methods that use point-level data, such as spatial intensity and K-functions, can better describe their geographic patterns. We applied these methods to a case study in New Castle County, DE to characterize the cross-sectional spatial relationship between tobacco retailers and smokers, finding that current smokers experience greater tobacco retail exposure and clustering relative to former smokers. We discuss how analysis at different geographic scales can provide complementary insights for tobacco control policy.     

2017年湖北省荆州市肾综合征出血热空间点格局分析

  目的  通过分析2017年春夏季、秋冬季荆州市肾综合征出血热（hemorrhagic fever with renal syndrome,HFRS）空间点格局分布特征,探讨其高发区域及原因,为公共卫生资源配置提供依据。  方法  个案数据来源于中国疾病预防信息系统,2017年3月-2017年8月为春夏季,2017年9月-2018年2月为秋冬季,采用Ripley's K函数和核密度估计进行空间点格局分析,比较2个季节空间点格局分布特征。  结果  2017年荆州市报告HFRS发病133例;有春夏季、秋冬季2个发病高峰;最强聚集距离春夏季为17.77 km、秋冬季为14.40 km;空间聚集中心春夏季在监利县北部,秋冬季转移到江陵县和沙市区南部。  结论  荆州市HFRS防控重点区域是江陵县、沙市区南部、监利县北部,重点人群为沙市区南部城乡结合部居民、大型工程沿线居民、聚集区域从事农业种植或小龙虾养殖的农民。     

Evaluating inter-hospital variability in mortality rates over time, allowing for time-varying effects.

In outcome studies, quality of care in various institutions is typically assessed by comparing observed to expected outcome rates, after adjusting for patients' case-mix factors in logistic regression models. However, differences in patterns of outcome rates over time, especially when there is a distinction between the determinants affecting early and later events, are rarely studied. We use six-month mortality after coronary artery bypass graft operation (CABG) as an example. We present a statistically valid approach to estimate expected survival curves for different subgroups, based on a Cox survival model with time-varying effects. Bootstrap confidence intervals around the expected survival curves are constructed. This approach is applied for examining the pattern of deviation of high-mortality hospitals after CABG. Implications for quality assessment in comparative outcome studies are discussed.     

Detection of spatial clusters: application to cancer survival as a continuous outcome

In this article, we develop the first detailed illustration of the use of a cluster detection method using a spatial scan statistic based on an exponential survival model. We use this approach to study the spatial patterns of survival of patients with stage III or stage IV colorectal cancer or with stage I/II, stage III, or stage IV lung cancer in the State of California and the County of Los Angeles (LA) diagnosed during 1988 through 2002. We present the location of the detected clusters of short survival or long survival and compute nonparametric estimates of survival inside and outside of those detected clusters confirming the survival pattern detected by the spatial scan statistic in both areas. In LA County, we investigate the possible relationship between the cluster locations and race, sex, and histology using nonparametric methods, and we compare socioeconomic factors such as education, employment, income, and health insurance inside and outside of the detected clusters. Finally, we evaluate the effect of related covariates on statistically significant long and short survival clusters detected in LA County using logistic regression models. This article illustrates a new way to understand survival patterns that may point to health disparities in terms of diagnosis and treatment patterns.     

Space–Time Clustering of Glioma Cannot be Attributed to Specific Histological Subgroups

We previously showed that infectious exposures may be involved in the aetiology of adult glioma, by analysing for space–time clustering using population-based data from the South of the Netherlands. Here we extended these analyses and describe in detail the space–time clustering patterns in glioma subgroups, gender and age-categories. Knox tests for space–time interactions between cases were applied with fixed thresholds of close in space, <5 km, and close in time, <1 year apart. We used the spatial coordinates of the addresses at diagnosis in the analyses. Tests were repeated replacing geographical distance with distance to the Nth nearest neighbour. N was chosen such that the mean distance was 5 km. Data were also analysed by a second order procedure based on K-functions. There was only statistically significant space–time clustering for oligodendroglioma. Clustering was present for adults aged 30–54 years and was more pronounced among males. Given the low prior probability of an infectious aetiology for this specific subgroup, these results should probably be interpreted as false-positive. We conclude that space–time clustering of glioma cannot be attributed to a specific glioma subgroup. The observed clustering in our previous study is therefore probably an overall effect within and between glioma subgroups.     

The uses of spatial analysis in medical geography: a review

This paper is a review of how geographers and others have used spatial analysis to study disease and health care delivery patterns. Point, line, area and surface patterns, as well as map comparisons and relative spaces are discussed. Problems encountered in applying spatial analytic techniques in medical geography are pointed out. The paper is intended to stimulate discussion about where medical geography can and should go in this area of study. Point pattern techniques include standard distance, standard deviational ellipses, gradient analysis and space and space-time clustering. Line methods include random walks, vectors and graph theory or network analysis. Under areas, location quotients, standardized mortality ratios, Poisson probabilities, space and space-time clustering, autocorrelation measures and hierarchical clustering are discussed. Surface techniques mentioned are isolines and trend surfaces. For map comparisons, Lorenz curves, coefficients of areal correspondence and correlation coefficients have been used. Case-control matching, acquaintance networks, multidimensional scaling and cluster analysis are examples of methods that are based on relative or non-metric spaces. The review gives rise to the discussion of several general points: problems encountered in spatial analysis, theory building and verification, the appropriate role of technique and computer use. Some suggestions are made for further use of spatial analytic techniques in medical geography: Monte Carlo simulation of point patterns, network analysis to study referral systems and health care for pastoralists, geographic information systems to assess environmental risk, difference mapping for disease and risk factor map comparisons and multidimensional scaling to measure social distance.     

Intra-urban dynamics of dengue epidemics in Belo Horizonte, Minas Gerais State, Brazil, 1996-2002

This study aimed to describe the temporal-spatial patterns of dengue epidemics in Belo Horizonte, Minas Gerais State, Brazil, from 1996 to 2002 and to analyze residential address as a proxy for exposure. Reported dengue cases were analyzed according to week of onset of symptoms and residential census tract. Local Moran's index was used to assess spatial autocorrelation of incidence coefficients, and recurrent census areas over different epidemic waves were also verified. Ripley's K-function was used to compare spatial distribution patterns between the two population groups, assuming that they were distributed differently around the city. A total of 99,559 dengue cases were analyzed, resulting in seven epidemic waves with different durations and intensities, with cases clustering in a small fraction of areas, thinning out both spatially and temporally. Distinct case distribution patterns were observed according to the two exposed groups, suggesting the need to improve the reporting of possible place of infection. The observed endemic pattern of the disease also requires specific strategies and poses a major challenge for health surveillance services.     

Distance and risk measures for the analysis of spatial data: a study of childhood cancers.

Three statistical approaches, used to detect spatial clusters of disease associated with a point source exposure, are applied to childhood cancer data for the city of San Francisco (1973-88). The distributions of incident cases of leukemia (51 cases), brain cancer (35 cases), and lymphatic cancer (37 cases) among individuals less than 21 years of age are described using three measures of clustering: distance on a geopolitical map, distance on a density equalized transformed map, and relative risk. The point source of exposure investigated is a large microwave tower located southwest of the center of the city (Sutro Tower). The three analytic approaches indicate that the patterns of the major childhood cancers are essentially random with respect to the point source. These results and a statistical model for spatial clustering are used to explore distance and risk measures in the analysis of spatial data. Both types of measures of spatial clustering are shown to perform similarly when a specific area of exposure can be defined.     

Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand

In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999-2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999-2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well.     

Additive isotonic regression models in epidemiology

Stone's method for assessing disease risk around a point source through isotonic regression is routinely used in spatial epidemiology. It is useful in situations where the relationship of risk with exposure (distance being commonly used as a surrogate variable) is assumed monotonic but otherwise of unknown form. This paper extends this method to non-spatial epidemiology, where typically two or more risk factors are present. The methodology described is based on the additive isotonic model approach of Bacchetti; versions appropriate to count (Poisson) data and case-control (binomial) data are described. In both cases, adjustment for covariates is incorporated, and a Monte Carlo method of hypothesis testing and interval estimation is presented. The methodology is illustrated through a case-control example concerning the analysis of the possible effect of preconceptional external ionizing radiation doses on the sex ratio at birth among children of fathers working at the Sellafield nuclear installation, Cumbria, U.K.     

Advancing insights into methods for studying environment-health relationships: a multidisciplinary approach to understanding Legionnaires' disease

Recent years have witnessed important advances in the analysis of spatially referenced health data. This paper uses GIS and point pattern modelling to address previously unanswered questions regarding the spatial epidemiology of Legionnaires' disease. We demonstrate a distance effect for proximity of residence to cooling towers; mixed support for a directional effect; and some evidence relating to multiple sources. In uncovering complex conceptual and technical problems in the spatial modelling of infection risk we also extended the limits of existing point pattern techniques. We advocate further multidisciplinary research to advance methodological developments for understanding spatial environment-health relationships.     

Point pattern analysis with spatially varying covariate effects,applied to the study of cerebrovascular deaths

This article proposes a modeling approach for handling spatial heterogeneity present in the study of the geographical pattern of deaths due to cerebrovascular disease.The framework involvesa point pattern analysis with components exhibiting spatial variation. Preliminary studies indicate that mortality of this disease and the effect of relevant covariates do not exhibit uniform geographic distribution. Our model extends a previously proposed model in the literature that uses spatial and non‐spatial variables by allowing for spatial variation of the effect of non‐spatial covariates. A number of relative risk indicators are derived by comparing different covariate levels, different geographic locations, or both. The methodology is applied to the study of the geographical death pattern of cerebrovascular deaths in the city of Rio de Janeiro. The results compare well against existing alternatives, including fixed covariate effects. Our model is able to capture and highlight important data information that would not be noticed otherwise, providing information that is required for appropriate health decision‐making. Copyright © 2014 John Wiley & Sons, Ltd.     

A Bayesian probit model with spatially varying coefficients for brain decoding using fMRI data

Recent advances in human neuroimaging have shown that it is possible to accurately decode how the brain perceives information based only on non‐invasive functional magnetic resonance imaging measurements of brain activity. Two commonly used statistical approaches, namely, univariate analysis and multivariate pattern analysis often lead to distinct patterns of selected voxels. One current debate in brain decoding concerns whether the brain's representation of sound categories is localized or distributed. We hypothesize that the distributed pattern of voxels selected by most multivariate pattern analysis models can be an artifact due to the spatial correlation among voxels. Here, we propose a Bayesian spatially varying coefficient model, where the spatial correlation is modeled through the variance‐covariance matrix of the model coefficients. Combined with a proposed region selection strategy, we demonstrate that our approach is effective in identifying the truly localized patterns of the voxels while maintaining robustness to discover truly distributed pattern. In addition, we show that localized or clustered patterns can be artificially identified as distributed if without proper usage of the spatial correlation information in fMRI data. Copyright © 2016 John Wiley & Sons, Ltd.     

四川光壳钉螺微卫星遗传变异的小尺度空间自相关分析

目的 采用小尺度空间自相关分析方法对四川省普格县的湖北钉螺自然居群微卫星(SSR)遗传变异的空间结构进行分析,探讨湖北钉螺自然居群遗传变异的空间分布特征.方法 选用5对SSR引物对湖北钉螺基因组DNA进行扩增,选择频率在15%～85%的72个SSR等位基因,运用等样本对频率方法划分14个距离等级分别计算空间自相关系数Moran's I,.结果 5对SSR引物经PCR扩增共得到274个等位基因,SSR等位基因的平均多态信息量高达0.965,表现出很高的遗传多态性.钉螺种群中39个SSR等位基因的遗传变异存在一定程度的空间结构,表现为不同模式的正空间自相关.根据这39个等位基因在14个距离等级上的平均Moran's I值,可以发现随着距离增大正空间自相关性逐渐减小,但未发现表现为负空间自相关的距离等级和等位基因.结论 普格县湖北钉螺种群中SSR遗传变异的空间分布,表现为随着距离增加而减少的正空间自相关性.     

Genetic structure of the poplar rust fungus Melampsora larici-populina: Evidence for isolation by distance in Europe and recent founder effects overseas.

Dispersal has a great impact on the genetic structure of populations, but remains difficult to estimate by direct measures. In particular, gradual and stochastic dispersal are often difficult to assess and to distinguish, although they have different evolutionary consequences. Plant pathogens, especially rust fungi, are suspected to display both dispersal modes, though on different spatial scales. In this study, we inferred dispersal capacities of the poplar rust fungus Melampsora larici-populina by examining the genetic diversity and structure of 13 populations from eight European and two overseas countries in the Northern hemisphere. M. larici-populina was sampled from both cultivated hybrid poplars and on the wild host, Populus nigra. The populations were analyzed with 11 microsatellite and 8 virulence markers. Although isolates displayed different virulence profiles according to the host plant, neutral markers revealed little population differentiation with respect to the type of host. This suggests an absence of reproductive isolation between populations sampled from cultivated and wild poplars. Conversely, studying the relationship between geographic and genetic structure allowed us to distinguish between isolation by distance (IBD) patterns and long distance dispersal (LDD) events. The European populations exhibited a significant IBD pattern, suggesting a regular and gradual dispersal of the pathogen over this spatial scale. Nonetheless, the genetic differentiation between these populations was low, suggesting an important gene flow on a continental scale. The two overseas populations from Iceland and Canada were shown to result from rare LDD events, and exhibited signatures of strong founder effects. Furthermore, the high genetic differentiation between both populations suggested that these two recent introductions were independent. This study illustrated how the proper use of population genetics methods can enable contrasted dispersal modes to be revealed.     

Scales of environmental justice: combining GIS and spatial analysis for air toxics in West Oakland, California

This paper examines the spatial point pattern of industrial toxic substances and the associated environmental justice implications in the San Francisco Bay Area, California, USA. Using a spatial analysis method called Ripley's K we assess environmental justice across multiple spatial scales, and we verify and quantify the West Oakland neighborhood as an environmental justice site as designated by the US Environmental Protection Agency. Further, we integrate the ISCST3 air dispersion model with Geographic Information Systems (GIS) to identify the number of people potentially affected by a particular facility, and engage the problem of non-point sources of diesel emissions with an analysis of the street network.     

A spatially discrete approximation to log-Gaussian Cox processes for modelling aggregated disease count data

In this paper, we develop a computationally efficient discrete approximation to log-Gaussian Cox process (LGCP) models for the analysis of spatially aggregated disease count data. Our approach overcomes an inherent limitation of spatial models based on Markov structures, namely, that each such model is tied to a specific partition of the study area, and allows for spatially continuous prediction. We compare the predictive performance of our modelling approach with LGCP through a simulation study and an application to primary biliary cirrhosis incidence data in Newcastle upon Tyne, UK. Our results suggest that, when disease risk is assumed to be a spatially continuous process, the proposed approximation to LGCP provides reliable estimates of disease risk both on spatially continuous and aggregated scales. The proposed methodology is implemented in the open-source R package SDALGCP .     

Efficient use of longitudinal CD4 counts and viral load measures in survival analysis

CD4 counts and viral loads are dynamic quantities that change with time in HIV-infected persons. Commonly used single summary measures, such as viral load set point or early CD4 count, do not explicitly account for changes in viral load or CD4 counts or other features of the overall time course of these measures. However, the efficient use of all repeated measurements within each subject is often a challenge made more difficult by sparse and irregular sampling over time. Here, we illustrate how functional principal component (FPC) analysis provides an effective statistical approach for exploiting the patterns in CD4 count and viral load data over time. We demonstrate the method by using data from Kenyan women who acquired HIV-1 during follow-up in a cohort that practices high-risk activities and were subsequently followed up prospectively from early infection. The FPC scores for each woman obtained using this method served as informative summary statistics for the CD4 count and viral load trajectories. Similar to baseline CD4 count or viral set point, the first FPC score can be interpreted as a single-value summary measure of an individual's overall CD4 count or viral load. However, unlike most single-value summaries of CD4 count or viral load trajectories, the first FPC score summarizes the dynamics of these quantities and is seen to reveal specific features of the trajectories associated with mortality in this cohort. Moreover, the FPC scores are shown to be a more powerful prognostic factor than other common summaries when used in survival analysis.