Evidence of localized clustering of gastroschisis births in North Carolina, 1999–2004

Examining the geographic distribution of birth defects can be useful in exploratory etiologic research. Identification of clusters of certain defects may uncover possible environmental or socio-economic risk factors and assist with the generation of hypotheses about underlying causes of these conditions. In North Carolina, the prevalence of gastroschisis, a serious abdominal wall defect, has increased over the past decade and anecdotal evidence from clinicians suggests the possibility of clustering of this condition. This study uses a spatial scan statistic to identify the location and extent of clusters of gastroschisis births in North Carolina between 1999 and 2004. Data on cases of gastroschisis were obtained from the North Carolina Birth Defect Monitoring Program (NCBDMP) and control births were chosen from all resident live births without birth defects contained in the North Carolina composite linked birth files. The clusters were controlled for five major risk factors (maternal age, race, parity, Medicaid status, maternal smoking) to ensure that the clusters were not artifacts of unequal population distribution. Results indicate a localized cluster of gastroschisis in the rural southern Piedmont of North Carolina which persists even after controlling for all major risk factors. Adjusting for these risk factors shifted the location of the cluster substantially, demonstrating the importance of adjusting for underlying population distribution. Since clusters persisted after adjusting for individual-level risk factors, environmental contaminants may explain the excess of gastroschisis cases. This study is among the first to assess spatial clustering of gastroschisis using GIS methods. This study also demonstrates the importance of controlling for covariates in spatial analysis and illustrates the usefulness of the spatial scan statistic in exploratory etiologic research.     

A model for space–time cluster detection using spatial clusters with flexible temporal risk patterns

Maps of estimated disease rates over multiple time periods are useful tools for gaining etiologic insights regarding potential exposures associated with specific locations and times. In this paper, we describe an extension of the Gangnon–Clayton model for spatial clustering to spatio‐temporal data. As in the purely spatial model, a large set of circular regions of varying radii centered at observed locations are considered as potential clusters, e.g. subregions with a different pattern of risk than the remainder of the study region. Within the spatio‐temporal model, no specific parametric form is imposed on the temporal pattern of risk within each cluster. In addition to the clusters, the proposed model incorporates spatial and spatio‐temporal heterogeneity effects and can readily accommodate regional covariates. Inference is performed in a Bayesian framework using MCMC. Although formal inferences about the number of clusters could be obtained using a reversible jump MCMC algorithm, we use local Bayes factors from models with a fixed, but overly large, number of clusters to draw inferences about both the number and the locations of the clusters. We illustrate the approach with two applications of the model to data on female breast cancer mortality in Japan and evaluate its operating characteristics in a simulation study. Copyright © 2010 John Wiley & Sons, Ltd.     

Impact of prior choice on local Bayes factors for cluster detection

In this paper, we evaluate the usefulness of local Bayes factors as a tool for spatial cluster detection. In particular, we consider whether local Bayes factors from models with a fixed, but overly large number of clusters can consistently identify the evidence for clustering for a variety of prior specifications for the cluster locations. We also investigate the robustness of the local Bayes factor to the number of clusters included in the model. We explore the impacts of prior choice for cluster location and the number of clusters on posterior inference for disease rates. We conduct the comparison by analysing data on 1990 breast cancer incidence in Wisconsin.     

A geographical analysis of breast cancer clustering in southern Ontario: generating hypotheses on environmental influences

This article presents the results of spatial analysis of breast cancer clustering in southern Ontario. Data from the Cancer Care Ontario were analyzed using the Scan Statistic at the level of county, with further analysis conducted within counties that were identified as primary clusters at the dissemination area level. The results identified five counties as primary clusters of women diagnosed with breast cancer between 1986 and 2002: Essex (relative risk [RR] =1.096-1.061; p<0.001), Lambton (RR=1.05-1.167), Chatham-Kent (RR=1.133-1.191), Niagara (RR=1.228-1.290) and Toronto (RR=1.152-1.146). The within county analysis revealed several DAs with significantly higher (RR>3, p<0.05) rates of breast cancer, and supports our hypothesis that breast cancer risk in southern Ontario may be associated with industrial and environmental (such as pesticides) pollutants. Further research is needed to verify the environmental links within the identified clusters.     

Identifying at-risk communities for action on cancer prevention: a case study in new brunswick (Canada) communities

Health statistics reported by large geographic area such as province, state, county or health region offer little insight into disease conditions at the community level where people live and work, where occupational and environmental exposures occur, and where industrial emissions are often concentrated. This study investigated overall patterns of cancer incidence and socioeconomic status (SES) among 14 communities in the province of New Brunswick (Canada). A multivariate ordination technique, hierarchical clustering, and permutation procedures were used to identify and test significance of community clusters and whether the overall pattern of SES was correlated with patterns of cancer among communities. Communities with significantly high or significantly low overall rates of cancers were identified, patterns that were not related to SES. The potential influence of age, small populations, diagnostic screening, smoking and environmental risk factors contributing to locally elevated cancer rates are discussed. Cancer incidence reported at smaller spatial scales provides health officials and researchers with a basis for identifying communities potentially at-risk and aids in the development of appropriate community-based risk reduction actions and cancer prevention.     

Census and geographic differences between respondents and nonrespondents in a case-control study of non-Hodgkin lymphoma

To quantify nonresponse bias and estimate its potential impact, the authors compared census-based socioeconomic and demographic factors and geographic locations among respondents and nonrespondents in a multicenter case-control study of non-Hodgkin lymphoma (1998-2000). Using a geographic information system, the authors mapped current addresses and linked them to the 2000 US Census database to determine group-level demographic and socioeconomic information. They used logistic regression analysis to compute the risk of being a nonrespondent, separately for cases and controls. They used spatial scan methods to evaluate spatial clustering at each study center. Among cases at one or more centers, nonresponse was significantly associated with non-White race, lower household income, a greater proportion of multiple-unit housing, fewer years of education, and living in a more urbanized area. For most factors, the authors observed similar patterns among controls, although findings were mostly nonsignificant. They found two nonrandom elliptical clusters in Los Angeles, California, and Detroit, Michigan, that disappeared after adjustment for the demographic factors. The authors determined the bias in non-Hodgkin lymphoma risk associated with census-tract educational level by comparing risks among respondents and all subjects. The bias was 8%, indicating that the socioeconomic and demographic differences between respondents and nonrespondents did not result in a large bias in the risk estimate for education.     

Late detection of breast and colorectal cancer in Minnesota counties: an application of spatial smoothing and clustering

Cancers detected at a later disease stage are associated with significantly higher mortality risk. Lack of uniformity in county-level cancer detection rates is thus of substantial interest to state health departments and other public health professionals. In this paper, we perform several spatial analyses of breast and colorectal cancer detection data for Minnesota counties for 1995-1997. We look for outliers and clusters in the late detection rates using a number of techniques: (i). applying various mapping schemes, (ii). smoothing the data using Bayesian methods implemented via Markov chain Monte Carlo, and (iii). applying maximum likelihood techniques to test for the presence of clusters and to identify the most likely clustersOur results suggest a fairly uniform spatial pattern in both sets of detection rates. Spatially smoothed rates did not reveal clusters of counties with significantly higher late detection risk, nor were county-level covariates (such as income, education, and race) particularly helpful in explaining the rates. However, our spatial clustering approach (using the scan statistic) did produce statistically significant clusters of counties which may indicate differences of practical importance for public health.     

Accounting for measurement error in biomarker data and misclassification of subtypes in the analysis of tumor data

A common paradigm in dealing with heterogeneity across tumors in cancer analysis is to cluster the tumors into subtypes using marker data on the tumor, and then to analyze each of the clusters separately. A more specific target is to investigate the association between risk factors and specific subtypes and to use the results for personalized preventive treatment. This task is usually carried out in two steps–clustering and risk factor assessment. However, two sources of measurement error arise in these problems. The first is the measurement error in the biomarker values. The second is the misclassification error when assigning observations to clusters. We consider the case with a specified set of relevant markers and propose a unified single‐likelihood approach for normally distributed biomarkers. As an alternative, we consider a two‐step procedure with the tumor type misclassification error taken into account in the second‐step risk factor analysis. We describe our method for binary data and also for survival analysis data using a modified version of the Cox model. We present asymptotic theory for the proposed estimators. Simulation results indicate that our methods significantly lower the bias with a small price being paid in terms of variance. We present an analysis of breast cancer data from the Nurses' Health Study to demonstrate the utility of our method. Copyright © 2016 John Wiley & Sons, Ltd.     

江苏省扬中市食管癌高危人群危险因素及其聚集性

目的 分析江苏省扬中市食管癌高危人群危险因素流行、聚集现状并探讨危险因素聚集的影响因素.方法 以上消化道癌早诊早治及食管癌专病队列项目为依托,运用多阶段分层整群抽样的方法,抽取扬中市2 591名40～69岁高危人群进行一对一式面访调查,采用单因素Logistic回归分析模型及多因素Logistic回归分析模型探讨危险因...     

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.     

Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation

Background  

Smoothing methods have been developed to improve the reliability of risk cancer estimates from sparsely populated geographical entities. Filtering local details of the spatial variation of the risk leads however to the detection of larger clusters of low or high cancer risk while most spatial outliers are filtered out. Static maps of risk estimates and the associated prediction variance also fail to depict the uncertainty attached to the spatial distribution of risk values and does not allow its propagation through local cluster analysis. This paper presents a geostatistical methodology to generate multiple realizations of the spatial distribution of risk values. These maps are then fed into spatial operators, such as in local cluster analysis, allowing one to assess how risk spatial uncertainty translates into uncertainty about the location of spatial clusters and outliers. This novel approach is applied to age-adjusted breast and pancreatic cancer mortality rates recorded for white females in 295 US counties of the Northeast (1970–1994). A public-domain executable with example datasets is provided.     

'Excellence' begets awareness. Long Island's Good Samaritan Hospital stresses areas of expertise

Good samaritan Hospital Medical Center in West Islip, N.Y. has created a solid reputation as a community hospital noted for its compassionate care, especially as it relates to mother/baby wellness. But, in the increasingly competitive health care world, it's necessary to aggressively market services. Good Samaritan in the last decade built "institutes of excellence" as is telling the community about them in a coordinated multiplicity of strategies using nearly every facet of marketing communications and advertising.     

Alcohol mortality: a comparison of spatial clustering methods

The identification of spatial clusters of alcohol mortality can be a key tool in identifying locations that are suffering from alcohol-related problems or are at risk of experiencing those types of problems. This study compares two methods for identifying statistically significant spatial clusters of county-level alcohol mortality rates in New York. One method utilizes a local indicator of spatial association to determine which groups of neighboring counties have rates that are significantly related to each other. The other method is a spatial scan technique that calculates a maximum likelihood ratio of cases relative to the underlying population to identify the group of counties that rejects the null hypothesis of "no clustering". The results show that because each technique bases its cluster detection on its own criteria, different counties are selected by each method. However, the overlap of the selections indicates that the two analytic methods illustrate different elements of the same clusters. Consequently, these spatial analytic techniques are seen as complimentary and are best used in tandem rather than individually. These findings suggest that multiple methods are a preferred approach to identifying clusters of alcohol-related mortality at the county level.     

西安市中老年社区居民脑卒中高危人群筛查结果及危险因素聚集性研究

目的 调查中老年社区居民的脑卒中高危人群现状,分析其危险因素聚集性特点。方法 采取整群随机抽样法,随机抽取西安市3个区（灞桥区、雁塔区和未央区）,每个区随机抽取3个社区,共9个社区,以辖区内≥40岁的常住居民做为调查对象进行问卷调查、体格调查及生理生化指标检测。按照《国家脑卒中高危人群筛查和干预项目》中规定对调查对象脑卒中高危情况进行评估,采用描述性分析方法对结果进行分析。结果 完成调查人数18 312人,其中男性8 483人,占46.32%;女性9 829人,占性53.68%。年龄40～93岁,其中50～59岁所占比例较高,为30.13%。共筛选出符合脑卒中高危人群特征的居民3 325例,高危率18.16%。不同性别和年龄的中老年社区居民的脑卒中高危人群检出率差异有统计学意义（P<0.05或P<0.01）。脑卒中高危人群中检出的8项危险因素前3项依次为体育运动缺乏（71.40%）、高血压（70.86%）和吸烟（47.67%）。3 325例脑卒中高危人群符合3项危险因素为1 880例（56.54%）,符合4项为894例（26.89%）,符合5项404例（12.15%）,符合...     

The independent associations of parity, age at first full term pregnancy, and duration of breastfeeding with the risk of breast cancer. Cancer and Steroid Hormone Study Group

Although the important influence of a woman's reproductive history on her risk of breast cancer is widely recognized, it is not clear whether this is wholly accounted for by the age at her first full-term pregnancy, or whether there are additional, independent influences of breastfeeding or number of children. To examine the respective contributions to the risk of breast cancer of these reproductive factors, we used logistic regression methods to analyze data from a multicenter case-control study, the Cancer and Steroid Hormone Study. Included in the analysis were 4599 women, 20-55 years of age, identified as having an initial diagnosis of breast cancer by one of eight collaborating population-based cancer registries. The 4536 controls were women of similar ages selected by random dialing of households with telephones in the same eight areas. As expected, age at first full-term pregnancy exerted a strong influence on the risk of breast cancer. However, after it and other potentially confounding factors had been controlled for, parity and duration of breastfeeding also had a strong influence on the risk of breast cancer. Compared with women of parity one, women of parity seven or greater had an adjusted relative risk of breast cancer of 0.59 (95% CL, 0.44-0.79). Compared with parous women who never breastfed, women who had breastfed for 25 months or more had an adjusted relative risk of 0.67 (0.52-0.85). These results do not support the supposed preeminent importance of age at first full-term pregnancy among the reproductive determinants of breast carcinogenesis. Resolution of this issue may have important implications for elucidating hormonal influences on breast cancer and for projecting future trends in the disease.     

基于空间点模式分析全球高致病性禽流感H5N1的空间分布特征

目的 探索全球高致病性禽流感（highly pathogenic avian influenza, HPAI）H5N1疫情的空间分布特征,识别高风险聚集区,为禽流感疫情监测、发展趋势提供科学依据。方法 收集和整理世界动物卫生组织和粮农组织疫情监测报告中的2004年1月～2009年12月间的 HPAI H5N1疫情,运用空间扫描统计量和空间聚类方法分析全球HPAI H5N1的空间分布。结果 空间扫描统计量分析显示疫情呈现非随机分布的特征,在空间分布上存在聚集性,共检测出15个高风险聚集区,主要集中在中欧、南欧、非洲东北部地区以及亚洲的东南部地区。空间聚类分析显示高风险聚集区主要集中于欧洲的德国、法国、瑞士和意大利,以及亚洲的东南部地区。结论 全球HPAI H5N1呈现空间聚集分布,高风险聚集区主要位于中欧、南欧以及东南亚,为重点监控区域。候鸟迁徙可能是跨洲远距离传播的重要途径。     

乳腺癌术后患者性调节和身体意象状况及因素分析

目的 通过SABIS工具了解乳腺癌术后患者性调节能力和身体意象状况,并探讨其影响因素。方法 采用横断面调查方法,应用乳腺癌患者性调节和身体意象量表（SABIS）、婚姻关系量表、社会支持量表和治疗期间乳腺癌患者症状问卷对2018年1月 - 2018年10月就诊于某地2家三甲医院的374例乳腺癌术后患者进行调查分析。结果 调查所得患者的SABIS均分是（43.58±8.61）分（满分70分）,婚姻关系、客观支持度、主观支持度、社会支持利用度、外在形象改变症状群、疾病相关症状群、治疗反应症状群及心境表达症状群进入多元回归方程,8个因子的累计贡献率为64.7%,各因素t值介于 - 7.725～4.277之间,P＜0.05。结论 乳腺癌术后患者性调节能力低下,身体意象状况普遍较差,婚姻关系、社会支持度及相关症状群是影响其水平的重要因素。临床医护人员应着重给予指导、干预,以达到最佳治疗效果。     

The CUSCORE test and the q-interval in cluster analyses of colon cancer and of lymphoma among asbestos workers

Exposure to a carcinogen is likely to be reflected by temporal clustering of cancer cases among the community members. Detection of such clustering is a complicated task in general and more so in a small workplace community with low turnover rate. In this study we applied the CUSCORE test and the q-interval statistic to lymphoma and colon cancer death data among 2200 asbestos workers. The CUSCORE test was applied in order to identify clusters, and the q-interval was used to provide clues related to the cause of the cluster. We also evaluated and tested the data according to the standardized mortality ratio (SMR). Significant increases in both colon cancer and lymphoma among asbestos workers in Israel (1987-1997) were demonstrated by the CUSCORE test but not so by the SMR. The fact that an apparent clustering was observed from the pattern of the q-intervals leads to the conclusion that the detected clusters are real.     

“Clusters” of CJD in Slovakia: The first statistically significant temporo-spatial accumulations of rural cases

A space-time analysis of clustering among 78 definite CJD cases who died in Slovakia (75) and an adjacent part of Hungary (3) during 1972 -1991 is presented. Two geographical rural accumulations of sporadic and familial CJD patients in the north and south of Central Slovakia were found. There was evidence of two significant clusters when spatial and temporal neighborhoods were defined by distances of 2,4,5 and 7 km within 1.5 and 2 years. Involvement of genetic mechanisms in the clusters was demonstrated, and the possible role of environmental (zoonotic) risk factors are considered.     

A Low-Glucose Eating Pattern Improves Biomarkers of Postmenopausal Breast Cancer Risk: An Exploratory Secondary Analysis of a Randomized Feasibility Trial

Postmenopausal breast cancer is the most common obesity-related cancer death among women in the U.S. Insulin resistance, which worsens in the setting of obesity, is associated with higher breast cancer incidence and mortality. Maladaptive eating patterns driving insulin resistance represent a key modifiable risk factor for breast cancer. Emerging evidence suggests that time-restricted feeding paradigms (TRF) improve cancer-related metabolic risk factors; however, more flexible approaches could be more feasible and effective. In this exploratory, secondary analysis, we identified participants following a low-glucose eating pattern (LGEP), defined as consuming energy when glucose levels are at or below average fasting levels, as an alternative to TRF. Results show that following an LGEP regimen for at least 40% of reported eating events improves insulin resistance (HOMA-IR) and other cancer-related serum biomarkers. The magnitude of serum biomarkers changes observed here has previously been shown to favorably modulate benign breast tissue in women with overweight and obesity who are at risk for postmenopausal breast cancer. By comparison, the observed effects of LGEP were similar to results from previously published TRF studies in similar populations. These preliminary findings support further testing of LGEP as an alternative to TRF and a postmenopausal breast cancer prevention strategy. However, results should be interpreted with caution, given the exploratory nature of analyses.