Use of a prospective space-time scan statistic to prioritize shigellosis case investigations in an urban jurisdiction

OBJECTIVE: A prospective space-time scan statistic was applied to Chicago's 2002 shigellosis surveillance data to evaluate its utility in objectively describing clusters and assisting in the prioritization of investigations. METHODS: The prospective space-time module of SaTScan, a free software available online, was used to identify "live" clusters of disease, meaning cases that were current as of the date of the analysis and strongly associated in place and time. Fifty-two separate space-time analyses were run; one simulation for each week of 2002. Identified clusters were described in terms of space, time, risk factors reported by involved case-patients, and cases' links to venue-associated outbreaks. RESULTS: Twelve live clusters were detected at the p < 0.05 significance level: two single-household clusters and 10 community clusters. The community clusters ranged in size from 194 to 367 census tracts (median = 294), and in disease burden from 21 to 41 cases (median = 29). Geographically, all of the community clusters were located in the west-central part of the city and had a temporal span of 28 days. Within the 10 community clusters, 15 different day care centers were identified as potential exposure settings for case-patients or their close contacts. CONCLUSIONS: The prospective space-time scan statistic offers local health departments an objective way of describing clusters of shigellosis cases. The method used in this study could help prioritize the assignment and investigation of cases, particularly when overall shigellosis incidence exceeds expected numbers or when an agency's resources are stressed by other events, such as outbreaks.     

Detecting COVID-19 Clusters at High Spatiotemporal Resolution,New York City,New York,USA, June–July 2020

A surveillance system that uses census tract resolution and the SaTScan prospective space-time scan statistic detected clusters of increasing severe acute respiratory syndrome coronavirus 2 test percent positivity in New York City, NY, USA. Clusters included one in which patients attended the same social gathering and another that led to targeted testing and outreach.     

A CUSUM framework for detection of space-time disease clusters using scan statistics

Several methods for timely detection of emerging clusters of diseases have recently been proposed. We focus our attention on one of the most popular types of method; a scan statistic. Different ways of constructing space-time scan statistics based on surveillance theory are presented. We bridge the ideas from space-time disease surveillance, public health surveillance and industrial quality control and show that previously suggested space-time scan statistics methods can be fitted into a general CUSUM framework. Crucial differences between the methods studied are due to different assumptions about the spatial process. An example is the specification of the spatial regions of interest for a possible cluster, another is the increased rate to be detected within a cluster. We evaluate the detection ability of the methods considering the possibility of a cluster emerging at any time during the surveillance period. The methods are applied to the detection of an increased incidence of Tularemia in Sweden.     

Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico.

OBJECTIVES: This article presents a space-time scan statistic, useful for evaluating space-time cluster alarms, and illustrates the method on a recent brain cancer cluster alarms in Los Alamos, NM. METHODS: The space-time scan statistic accounts for the preselection bias and multiple testing inherent in a cluster alarm. Confounders and time trends can be adjusted for. RESULTS: The observed excess of brain cancer in Los Alamos was not statistically significant. CONCLUSIONS: The space-time scan statistic is useful as a screening tool for evaluating which cluster alarms merit further investigation and which clusters are probably chance occurrences.     

A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring

Background  

Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindrical space-time scan statistic has been used extensively for disease surveillance along with the SaTScan software. In the purely spatial setting, many different methods have been proposed to detect spatial disease clusters. In particular, some spatial scan statistics are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic.     

The use of outbreak information in the interpretation of clustering of reported cases of Escherichia coli O157 in space and time in Alberta, Canada, 2000-2002

We obtained a list of all reported cases of Escherichia coli O157 in Alberta during the 2000-2002 period, and using scan statistics we identified yearly temporal and spatial clusters of reported cases of E. coli O157 during the summer and in southern Alberta. However, the location of the spatial cluster in the south was variable among years. The impact of using both outbreak and sporadic data or only sporadic data on the identification of spatial and temporal clusters was small when analysing individual years, but the difference between spatial clusters was pronounced when scanning the entire study period. We also identified space-time clusters that incorporated known outbreaks, and clusters that were suggestive of undetected outbreaks that we attempted to validate with molecular data. Our results suggest that scan statistics, based on a space-time permutation model, may have a role in outbreak investigation and surveillance programmes by identifying previously undetected outbreaks.     

2011-2012年武威市凉州区胃癌时空特征分析

目的 探究2011-2012年武威市凉州区胃癌发病的时空分布特征。方法 利用空间经验贝叶斯平滑、时空扫描统计量分析方法,对胃癌发病数据进行分析。结果 平滑后的胃癌发病率高低值地区分布集中,有明显的聚集趋势。纯空间扫描与时空扫描均探测到该地区胃癌发病存在聚集性,纯空间扫描结果包括永昌镇、五和乡、洪祥镇等三个聚集区,扫描时间为2011-2012年;时空扫描同样得到三个聚集区,但在聚集区类别和大小上与空间扫描存在差异,其聚集时间为2011年1月1日～2011年12月31日。男女性时空扫描结果表明男女性胃癌发病也具有聚集性,且女性聚集区范围大于男性。结论 武威市胃癌发病持续走高,防控工作严峻。结合空间经验贝叶斯平滑、时空扫描统计量方法,能够克服区域人口间的差异,准确的找出胃癌空间分布特征及时空聚集性。     

A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism

Since the intentional dissemination of anthrax through the US postal system in the fall of 2001, there has been increased interest in surveillance for detection of biological terrorism. More generally, this could be described as the detection of incident disease clusters. In addition, the advent of affordable and quick geocoding allows for surveillance on a finer spatial scale than has been possible in the past. Surveillance for incident clusters of disease in both time and space is a relatively undeveloped arena of statistical methodology. Surveillance for bioterrorism detection, in particular, raises unique issues with methodological relevance. For example, the bioterrorism agents of greatest concern cause initial symptoms that may be difficult to distinguish from those of naturally occurring disease. In this paper, the authors propose a general approach to evaluating whether observed counts in relatively small areas are larger than would be expected on the basis of a history of naturally occurring disease. They implement the approach using generalized linear mixed models. The approach is illustrated using data on health-care visits (1996-1999) from a large Massachusetts managed care organization/multispecialty practice group in the context of syndromic surveillance for anthrax. The authors argue that there is great value in using the geographic data.     

深圳市一期和二期梅毒时空分布分析

目的 了解深圳市近年来一期和二期梅毒的时空分布特征,为政府财政投入方向和防控措施提供参考.方法 利用深圳市2005-2009年一期和二期梅毒监测数据(n=11 303),以病例中现住址和诊断日期作为地理和时间变量,采用SaTScan软件实现各年的时空扫描分析.结果 单纯时空扫描发现:(1)2005-2009年深圳市梅毒病例集群主要在宝安区和南山交界的南山、南头、新安、西乡,靠近香港的东门、蛇口、福田以及娱乐业比较发达的龙华、华富、黄贝、翠竹等街道,这些聚集区域在2005-2008年变化不大,但是2009年出现了9个新的集群.其中各年最可能的集群分别位于龙华(2005年,P≤0.001,RR=3.34)、翠竹(2006年,P≤0.001,RR=9.59)、华富(2007、2008年,P≤0.001,RR值分别为4.18和4.75)和翠竹(2009年,P≤0.001,RR=8.02).(2)2005-2009年时空重排扫描分析发现16个集群,与单纯空间扫描发现的聚集区域相似但有差异,最可能的集群是2006年的桂园街道.结论 时空分析方法可有效描述疾病聚集,对深圳市近年来一期和二期梅毒监测数据进行时空分析,明确各街道各时段的梅毒变化情况,发现梅毒主要聚集区域,为防治工作提供信息.     

A space-time cluster of adverse events associated with canine rabies vaccine

Electronic medical records of a large veterinary practice were used for surveillance of potential space-time clustering of adverse events associated with rabies vaccination in dogs. The study population was 257,564 dogs vaccinated in 169 hospitals in 13 US metropolitan areas during a 24-month period. Using a scan statistic for population rate data, significant space-time clusters were identified involving the Atlanta and Tampa/St. Petersburg areas during a 4-month period. Separate spatial-temporal analyses of these cities using coordinates for individual address coordinates identified one significant patient cluster (P=0.002), associated with a 23.26 km-radius area in Atlanta (20 adverse events in 702 dogs; 2.85%) from November 2002 through February 2003. This percentage of adverse events was significantly increased after adjustment for host-related factors and the number of concurrent vaccinations.     

Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data

Timely detection of clusters of localized influenza activity in excess of background seasonal levels could improve situational awareness for public health officials and health systems. However, no single data type may capture influenza activity with optimal sensitivity, specificity, and timeliness, and it is unknown which data types could be most useful for surveillance. We compared the performance of 10 types of electronic clinical data for timely detection of influenza clusters throughout the 2007/08 influenza season in northern California. Kaiser Permanente Northern California generated zip code-specific daily episode counts for: influenza-like illness (ILI) diagnoses in ambulatory care (AC) and emergency departments (ED), both with and without regard to fever; hospital admissions and discharges for pneumonia and influenza; antiviral drugs dispensed (Rx); influenza laboratory tests ordered (Tests); and tests positive for influenza type A (FluA) and type B (FluB). Four credible events of localized excess illness were identified. Prospective surveillance was mimicked within each data stream using a space-time permutation scan statistic, analyzing only data available as of each day, to evaluate the ability and timeliness to detect the credible events. AC without fever and Tests signaled during all four events and, along with Rx, had the most timely signals. FluA had less timely signals. ED, hospitalizations, and FluB did not signal reliably. When fever was included in the ILI definition, signals were either delayed or missed. Although limited to one health plan, location, and year, these results can inform the choice of data streams for public health surveillance of influenza.     

Simulated anthrax attacks and syndromic surveillance

We measured sensitivity and timeliness of a syndromic surveillance system to detect bioterrorism events. A hypothetical anthrax release was modeled by using zip code population data, mall customer surveys, and membership information from HealthPartners Medical Group, which covers 9% of a metropolitan area population in Minnesota. For each infection level, 1,000 releases were simulated. Timing of increases in use of medical care was based on data from the Sverdlovsk, Russia, anthrax release. Cases from the simulated outbreak were added to actual respiratory visits recorded for those dates in HealthPartners Medical Group data. Analysis was done by using the space-time scan statistic. We evaluated the proportion of attacks detected at different attack rates and timeliness to detection. Timeliness and completeness of detection of events varied by rate of infection. First detection of events ranged from days 3 to 6. Similar modeling may be possible with other surveillance systems and should be a part of their evaluation.     

Use of the local Knox statistic for the prospective monitoring of disease occurrences in space and time

The detection of clusters of events occurring close together both temporally and spatially is important in finding outbreaks of disease within a geographic region. The Knox statistic is often used in epidemiology to test for space-time clustering retrospectively. For quicker detection of epidemics, prospective methods should be used in which observed events in space and time are assessed as they are recorded. The cumulative sum (CUSUM) surveillance method for monitoring the local Knox statistic tests for space-time clustering each time there is an incoming observation. We consider the design of this control chart by determining the in-control average run length (ARL) performance of the CUSUM chart for different space and time closeness thresholds as well as for different control limit values. We also explain the effect of population density and region shape on the in-control ARL and discuss other distributional issues that should be considered when implementing this method.     

Space-time pattern of hepatitis A in Spain, 1997-2007

In Spain hepatitis A is a compulsory notifiable disease and individual cases are reported to the national epidemiological surveillance network. Incidence rates show variations in different regions. The aim of this study was to analyse the space-time pattern of hepatitis A risk at municipal level in Spain and at global and local levels during the period 1997-2007. At global level we used two estimates of risk: the standardized incidence ratio (SIR) and the posterior probability that the smoothed relative risk is >1 (PP). At local level we used the scan statistic method to analyse the space-time clusters. The SIR and significant PP (>0·8) showed the highest risk concentrated in areas of the Mediterranean coast. The most likely cluster gave a relative risk of 53·530. These spatial statistics methodologies can be complementary tools in the epidemiological surveillance of infectious diseases.     

Comparing spatio-temporal clusters of arthropod-borne infections using administrative medical claims and state reported surveillance data

Considered separately, notifiable disease registries and medical claims data have certain advantages (e.g., consistent case definitions and electronic records, respectively) and limitations (e.g., incomplete reporting and coding errors, respectively) within disease outbreak research. Combined however, these data could provide a more complete source of information. Using a retrospective space-time permutation scan statistic, zoonotic case information from a state registry system (TDH) was compared with administrative medical claims information from a managed care organization (MCO) to examine how data sources differ. Study observations included case information for four tick-borne (Lyme disease, ehrlichiosis, Rocky Mountain spotted fever, tularemia) and two mosquito-borne diseases (West Nile virus, La Crosse viral encephalitis) occurring in Tennessee. One hundred and three clusters were detected, of which nine were significant (P<0.05). Considering only significant clusters, no spatial or temporal overlapping between data sources occurred. In conclusion, data integration efforts and data limitations should be considered to provide more comprehensive case information.     

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain

Background

The Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated.

Results

A fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. Finally, those clusters are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. An application for dengue fever in a small Brazilian city is presented.

Conclusions

The ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Furthermore, as VBScan also incorporates topological information from the point neighborhood structure, in addition to the usual geometric information, it is more robust than purely geometric methods such as the elliptic scan. Those advantages were illustrated in a real setting for dengue fever space-time clusters.     

2005年成都市麻疹模拟实时监测与预警研究

目的探索前瞻性时空重排扫描统计量在麻疹早期预警中的应用。方法模拟实时监测,采用前瞻性时空重排扫描统计量,对成都市2005年麻疹数据进行逐日前瞻性分析。结果以6月份预警结果为例,6月10日发出预警信号,该预警信号包括的天数为2 d,所包括的乡镇街道为武侯区的簇桥街道和金花桥街道。结论前瞻性时空重排扫描统计量能充分利用数据信息,及时探测到传染病的聚集性发病。     

A scan statistic for continuous data based on the normal probability model

Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight. For such continuous data, we present a scan statistic where the likelihood is calculated using the the normal probability model. It may also be used for other distributions, while still maintaining the correct alpha level. In an application of the new method, we look for geographical clusters of low birth weight in New York City.     

Identification of geographic clusters for temporal heterogeneity with application to dengue surveillance

Identifying transmission of hot spots with temporal trends is important for reducing infectious disease propagation. Cluster analysis is a particularly useful tool to explore underlying stochastic processes between observations by grouping items into categories by their similarity. In a study of epidemic propagation, clustering geographic regions that have similar time series could help researchers track diffusion routes from a common source of an infectious disease. In this article, we propose a two-stage scan statistic to classify regions into various geographic clusters by their temporal heterogeneity. The proposed scan statistic is more flexible than traditional methods in that contiguous and nonproximate regions with similar temporal patterns can be identified simultaneously. A simulation study and data analysis for a dengue fever infection are also presented for illustration.     

Geographic clustering of nonmedical exemptions to school immunization requirements and associations with geographic clustering of pertussis

School immunization requirements are important in controlling vaccine-preventable diseases in the United States. Forty-eight states offer nonmedical exemptions to school immunization requirements. Children with exemptions are at increased risk of contracting and transmitting vaccine-preventable diseases. The clustering of nonmedical exemptions can affect community risk of vaccine-preventable diseases. The authors evaluated spatial clustering of nonmedical exemptions in Michigan and geographic overlap between exemptions clusters and clusters of reported pertussis cases. Kulldorf's scan statistic identified 23 statistically significant census tract clusters for exemption rates and 6 significant census tract clusters for reported pertussis cases between 1993 and 2004. The time frames for significant space-time pertussis clusters were August 1993-September 1993, August 1994-February 1995, May 1998-June 1998, April 2002, May 2003-July 2003, and June 2004-November 2004. Census tracts in exemptions clusters were more likely to be in pertussis clusters (odds ratio = 3.0, 95% confidence interval: 2.5, 3.6). The overlap of exemptions clusters and pertussis clusters remained significant after adjustment for population density, proportion of racial/ethnic minorities, proportion of children aged 5 years or younger, percentage of persons below the poverty level, and average family size (odds ratio = 2.7, 95% confidence interval: 2.2, 3.3). Geographic pockets of vaccine exemptors pose a risk to the whole community. In addition to monitoring state-level exemption rates, health authorities should be mindful of within-state heterogeneity.