Electronic event-based surveillance for monitoring dengue, Latin America

The current dengue epidemic in Latin America represents a major threat to health. However, surveillance of affected regions lacks timeliness and precision. We investigated real-time electronic sources for monitoring spread of dengue into new regions. This approach could provide timely estimates of changes in distribution of dengue, a critical component of prevention and control efforts.     

三种白纹伊蚊监测方法在登革热风险指示中的适用性探讨

目的  了解三种常用白纹伊蚊监测方法在登革热风险指示中的适用性。 方法  2017年12月~2018年7月,在广州市白云区两农村同时用三种监测方法进行白纹伊蚊幼虫和成蚊密度监测,并收集同期气象数据和登革热病例情况。 结果  幼虫和成蚊密度消长均受气温影响,各监测指标反映的白纹伊蚊密度消长趋势一致,但幼虫与成蚊密度存在差异。2017年12月-2018年2月,两监测点布雷图指数为5.66~24.53,指示风险为1~3级,诱蚊诱卵指数和成蚊密度指数分别为0~4.00和0~1只/人·h,指示风险均为0级;3月份,布雷图指数反映风险等级为3级,而诱蚊诱卵指数分别为2.13和3.77,风险为0级,成蚊密度指数分别为4只/人·h和6只/人·h,指示风险为1级和2级;4月份之后,各监测指标反映的登革热风险等级趋于一致。 结论  布雷图指数指示的登革热风险总是高于或等于诱蚊诱卵指数和成蚊密度指数指示的风险。诱蚊诱卵指数和成蚊密度指数能较敏感地反映白纹伊蚊的活动情况,指示登革热风险的准确性高。     

Fast subset scan for multivariate event detection

We present new subset scan methods for multivariate event detection in massive space–time datasets. We extend the recently proposed ‘fast subset scan’ framework from univariate to multivariate data, enabling computationally efficient detection of irregular space–time clusters even when the numbers of spatial locations and data streams are large. For two variants of the multivariate subset scan, we demonstrate that the scan statistic can be efficiently optimized over proximity‐constrained subsets of locations and over all subsets of the monitored data streams, enabling timely detection of emerging events and accurate characterization of the affected locations and streams. Using our new fast search algorithms, we perform an empirical comparison of the Subset Aggregation and Kulldorff multivariate subset scans on synthetic data and real‐world disease surveillance tasks, demonstrating tradeoffs between the detection and characterization performance of the two methods. Copyright © 2012 John Wiley & Sons, Ltd.     

天津市社区流感监测时空聚集性分析

目的 对天津市社区(村)进行流感监测及时空聚集性分析,为及时预警、有效处置流感聚集性区域提供理论依据.方法 2009年10月-2011年2月,在汉沽区汉沽街及4个社区卫生服务站、大田镇卫生院及6个村卫生所进行社区流感样病例每日监测,对所有病例标本进行流感快速检测,阳性者采集咽拭子通过Real-time PCR检测流感病毒核酸,采用SaTScan软件时空重排模型分析流感聚集性.结果 流感社区监测显示,2009年10月因甲型H1N1流感大流行出现一个大高峰,证明甲型H1N1流感大流行期间,农村也出现了甲型H1N1流感暴发流行的情况,2010/2011年度流感流行季疫情均较2009年同期明显下降;时空重排扫描分析发现10个有统计学意义的集群,2009年流感样病例(ILI)聚集性区域主要集中在汉沽街、大田镇及周边村(新立村、芦家坞)、大王庄、大田镇及周边村(新立村、大王庄),最有可能的集群为汉沽街(RR =27.44,P=0.0001),其他区域主要为次要集群;2010年流感疫情较平稳,只有大田镇小马杓村出现ILI聚集,并且为最优可能集群(RR=12.53,P=0.0001);经核实确认2起甲型H1N1流感暴发.结论 在社区进行流感时空聚集性分析,更准确地发现聚集性区域,早期预警,在公共卫生实践中有一定应用价值.     

广州市登革热媒介的监测分析

目的总结、评价广州市近年来登革热媒介的监测情况。方法采用布雷图指数、标准间指数、诱蚊诱卵器指数等方法监测和调查白纹伊蚊密度,同时对监测和调查所采集的白纹伊蚊样本用实时荧光逆转录聚合酶链反应进行病毒核酸检测,并对阳性结果反应产物进行序列测定。结果白纹伊蚊幼虫密度呈下降趋势。布雷图指数从2002年的11.99降到2008年的4.59,标准间指数从2003年的4.45降到2008年的0.73。但在登革热高发季节,白纹伊蚊幼虫的密度指数仍处于危险的阈值范围;流行年份白纹伊蚊密度高于非流行年份,且密度高峰提前。从采自多个地点的白纹伊蚊体内检测出登革病毒核酸,经分型鉴定为登革病毒1型。结论采用多种方法和密度指标,有利于客观地评估白纹伊蚊的密度及其防治效果,而白纹伊蚊携带登革病毒的监测则有助于对登革热流行趋势的评估和防控策略的调整。     

Clustered spatio‐temporal varying coefficient regression model

In regression analysis for spatio‐temporal data, identifying clusters of spatial units over time in a regression coefficient could provide insight into the unique relationship between a response and covariates in certain subdomains of space and time windows relative to the background in other parts of the spatial domain and the time period of interest. In this article, we propose a varying coefficient regression method for spatial data repeatedly sampled over time, with heterogeneity in regression coefficients across both space and over time. In particular, we extend a varying coefficient regression model for spatial‐only data to spatio‐temporal data with flexible temporal patterns. We consider the detection of a potential cylindrical cluster of regression coefficients based on testing whether the regression coefficient is the same or not over the entire spatial domain for each time point. For multiple clusters, we develop a sequential identification approach. We assess the power and identification of known clusters via a simulation study. Our proposed methodology is illustrated by the analysis of a cancer mortality dataset in the Southeast of the U.S.     

Fast Multidimensional Subset Scan for Outbreak Detection and Characterization

Objective

We present Multidimensional Subset Scan (MD-Scan), a new method for early outbreak detection and characterization using multivariate case data from individuals in a population. MD-Scan extends previous work on multivariate event detection by identifying the characteristics of the affected subpopulation, and enables more timely and accurate detection while maintaining computational tractability.

Introduction

The multivariate linear-time subset scan (MLTSS) [1] extends previous spatial and subset scanning methods [2–3] to achieve timely and accurate event detection in massive multivariate datasets, efficiently optimizing a likelihood ratio statistic over proximity-constrained subsets of locations and all subsets of the monitored data streams. However, some disease outbreaks may only affect a sub-population of the monitored population (age group, gender, individuals engaging in a specific high-risk behavior, etc.), and MLTSS is unable to use this additional information to enhance detection ability.

Methods

Rather than using the aggregate counts for each monitored location and data stream, we assume a set of multivariate data records representing each affected individual, with attributes such as date, home zip code, prodrome, gender, and age decile. MD-Scan jointly optimizes the likelihood ratio statistic over subsets of the values for each monitored attribute, identifying a space-time region (subset of locations and time steps) and subpopulation (including gender(s) and age groups) where the number of recent cases for a subset of the monitored prodromes is significantly higher than expected. To do so, the linear-time subset scanning property [3] is used to efficiently and exactly optimize over subsets of a given attribute, conditioned on the current subsets of all other attributes. MD-Scan then iterates over all attributes until convergence to a local optimum, and performs multiple random restarts to approach the global optimum. Additional constraints can be incorporated into each conditional optimization step, including spatial proximity, temporal contiguity, and connectedness. More details are provided in [4].

Results

We evaluated MD-Scan using simulated disease outbreaks injected into real-world Emergency Department data from Allegheny County, PA. Each outbreak was assumed to differentially affect a specific sub-population (e.g. “adult females” or “children and the elderly”). MD-Scan achieved significantly earlier detection than MLTSS when the distribution of injected cases for the monitored attributes was sufficiently different from the background data, particularly when multiple attributes were affected or the inject was biased toward a less common attribute value. For simulated gender-specific and age-biased injects which affected only children and the elderly, MD-Scan detected over one day faster than MLTSS, and achieved 10% higher spatial accuracy. MD-Scan was also able to accurately identify the affected age and gender groups (Figure 1), while MLTSS does not characterize the affected subpopulation. Runtime of MD-Scan, while 9× slower than MLTSS, was still extremely fast, requiring an average of 4.15 seconds per day of data.

Conclusions

Our results demonstrate that MD-Scan is able to accurately identify the subpopulation affected by an outbreak, as represented by a subset of values for each monitored attribute. Additionally, MD-Scan substantially improves timeliness and accuracy of detection for outbreaks which differentially affect a subset of the monitored population. Detection performance was further enhanced by incorporating additional constraints such as spatial proximity and graph connectivity into the iterative MD-Scan procedure.Open in a separate window     

Estimation of expected dengue seroprevalence from passive epidemiological surveillance systems in selected areas of Argentina: A proxy to evaluate the applicability of dengue vaccination

BackgroundCurrent recommendations about dengue vaccination by the World Health Organization depend on seroprevalence levels and serological status in populations and individuals. However, seroprevalence estimation may be difficult due to a diversity of factors. Thus, estimation through models using data from epidemiological surveillance systems could be an alternative procedure to achieve this goal.ObjectiveTo estimate the expected dengue seroprevalence in children of selected areas in Argentina, using a simple model based on data from passive epidemiological surveillance systems.MethodsA Markov model using a simulated cohort of individuals from age 0 to 9 years was developed. Parameters regarding the reported annual incidence of dengue, proportion of inapparent cases, and expansion factors for outpatient and hospitalized cases were considered as transition probabilities. The proportion of immune population at 9 years of age was taken as a proxy of the expected seroprevalence, considering this age as targeted for vaccination. The model was used to evaluate the expected seroprevalence in Misiones and Salta provinces and in Buenos Aires city, three settings showing different climatic favorability for dengue.ResultsThe estimates of the seroprevalence for the group of 9-year-old children for Misiones was 79% (95%CI:46–100%), and for Salta 22% (95%CI:14–30%), both located in northeastern and northwestern Argentina, respectively. Buenos Aires city, from central Argentina, showed a likely seroprevalence of 7% (95%CI: 3–11%). According to the deterministic sensitivity analyses, the parameter showing the highest influence on these results was the probability of inapparent cases.ConclusionsThis model allowed the estimation of dengue seroprevalence in settings where this information is not available. Particularly for Misiones, the expected seroprevalence was higher than 70% in a wide range of scenarios, thus in this province a vaccination strategy directed to seropositive children of >9 years should be analyzed, including further considerations as safety, cost-effectiveness, and budget impact.     

Detection of spatial disease clusters with LISA functions

Detection of disease clusters is an important tool in epidemiology that can help to identify risk factors associated with the disease and in understanding its etiology. In this article we propose a method for the detection of spatial clusters where the locations of a set of cases and a set of controls are available. The method is based on local indicators of spatial association functions (LISA functions), particularly on the development of a local version of the product density, which is a second-order characteristic of spatial point processes. The behavior of the method is evaluated and compared with Kulldorff's spatial scan statistic by means of a simulation study. It is shown that the LISA method yields high sensitivity and specificity when it is used to detect simulated clusters of different sizes and shapes. It also performs better than the spatial scan statistic when they are used to detect clusters of irregular shape; however, it presents relatively high type I error in situations where the number of cases is high. Both methods are applied for detecting spatial clusters of kidney disease in the city of Valencia, Spain, in the year 2008.     

A spatial scan statistic for compound Poisson data

The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease‐related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease‐related visits. Copyright © 2013 John Wiley & Sons, Ltd.     

浙江省2005-2011年肺结核流行特征与空间聚集性

目的 分析浙江省2005-2011年肺结核的流行特点,探讨其空间聚集特征。方法 综合肺结核报告发病率、空间自相关分析法和单纯空间扫描统计量,以区县级为单位对浙江省2005-2011年肺结核监测数据进行分析。结果 2005-2011年浙江省90个区县共报告新发肺结核239080例,年均报告发病率为67.50/10万。发病以男性为主,男女占比为2.18:1;年龄分布以15~60岁为主,占75.5%;职业分布以农民为主,占44.9%,其次是民工和工人,分别占22.0%和10.2%。空间自相关分析和单纯空间扫描统计量均发现浙江省西部衢州市的绝大多数区县以及杭州市的部分区县为肺结核高发病率聚集地区,而嘉兴市、宁波市和丽水市则为低发病率聚集地区。结论 浙江省肺结核发病率总体呈下降趋势,发病率低于全国平均水平;农村的男性青壮年是肺结核主要易感人群;衢州市是浙江省肺结核的主要发病聚集区域。     

Space–time prospective surveillance based on Knox local statistics

We studied a surveillance system to prospectively monitor the emergence of space–time clusters in point pattern of disease events. Its aim is to detect a cluster as soon as possible after its emergence, and it is also desired to keep the rate of false alarms at a controlled level. The method is a modification from a previous proposal based on a local version of the Knox statistic and which examined a retrospective surveillance scenario, looking for the earliest time in the past that change could have been deemed to occur. We modify this method to take into account the prospective case, being able then to fix the serious difficulties found by other authors. We evaluated the surveillance system in several scenarios, including without and with emerging clusters, checking distributional assumptions, and assessing performance impacts of different emergence times, shapes, extent, and intensity of the emerging clusters. Our conclusion is that the space–time surveillance system based on local Knox statistics is very efficient in its statistical properties, and it is appealing to epidemiologists and public health officials because it is simple to use and easily understandable. This makes it a promising candidate to practical use by public health official agencies. Copyright © 2014 John Wiley & Sons, Ltd.     

An improved cumulative sum‐based procedure for prospective disease surveillance for count data in multiple regions

We present an improved procedure for detecting outbreaks in multiple spatial regions using count data. We combine well‐known methods for disease surveillance with recent developments from other areas to provide a more powerful procedure that is still relatively simple and fast to implement. Disease counts from neighboring regions are aggregated to compute a Poisson cumulative sum statistic for each region of interest. Instead of controlling the average run length criterion in the monitoring process, we instead utilize the FDR, which is more appropriate in a public health context. Additionally, p‐values are used to make decisions instead of traditional critical values. The use of the FDR and p‐values in testing allows us to utilize recently developed multiple testing methodologies, greatly increasing the power of this procedure. This is verified using a simulation experiment. The simplicity and rapid detection ability of this procedure make it useful in disease surveillance settings. The procedure is successfully applied in detecting the 2011 Salmonella Newport outbreak in 16 German federal states. Copyright © 2016 John Wiley & Sons, Ltd.     

2014—2018年深圳市登革热疫情流行特征分析

目的 分析2014—2018年深圳市登革热疫情流行特征,评价防控效果,为制定防控策略提供依据。 方法 收集2014—2018年深圳市所报告登革热病例的个案信息和“中国疾病预防控制信息系统”报告的病例信息,采用office 2010和SPSS 11.5对疫情资料进行整理和分析。 结果 2014—2018年深圳市累计报告登革热病例825例,发病率为0.26/10万～4.21/10万,本地病例499例,其中男性312例(年均发病率0.97/10万),女性187例(年均发病率0.70/10万),男女发病率差异有统计学意义(χ²=12.23,P＜0.001);输入病例326例,男性199例(年均发病率0.61/10万),女性127例(年均发病率0.48/10万),男女发病率差异有统计学意义(χ²=5.24,P＜0.05)。发病高峰期为9—11月(688例,83.39%)。2014年和2018年波及范围较广(波及街道比分别为68.66%和37.31%)。以工人(252例)、家务及待业(197例)和商业服务(142例)为主,合计占71.55%。 结论 2014—2018年深圳市登革热主要流行于9—11月,青壮年为高危人群。因此在疾病高发季节前加强以杀灭成蚊和清除蚊媒孳生地为主的综合病媒控制措施、针对高危人群开展防蚊宣传等措施可有效控制登革热传播。     

医院感染实时监控系统的开发与应用

目的 解决医院感染在线、实时监测问题,实现感染病例智能化识别与预警,并进行实时干预-反馈,提高监测效率,全面提升预防控制水平.方法 依据《医院感染监测规范》和《医院感染诊断标准》等标准,结合实际工作经验,借鉴国内外监测系统的优点,利用计算机网络技术,设计并开发医院感染实时监控系统(RT-NISS).结果 RT-NISS通过数据访问中间件技术,采集医院信息管理系统(HIS)、实验室信息管理系统(LIS)、电子病历管理系统(EMR)等相关数据,建立起动态的感染信息基础数据库,实现了对患者从入院到出院全过程在线监测;通过嵌入专业筛查策略,实现了疑似感染病例智能识别,并进行个案预警,方便专职人员判别;通过建立交互平台,实现了感染病例实时推送、精确诊断、干预与反馈,使专职人员与临床医师共同参与感染诊断与预防控制;通过建立暴发预警机制,实现了暴发隐患的及时发现;通过规范的监测流程和计算方法,进行全院综合性监测和目标性监测,并实现了全面、准确统计分析结果的输出;通过先进的计算机技术,实现了系统的可操作性、高效性、安全性和开放性.结论 RT-NISS是高效率的医院感染监测与预防控制系统,通过准确、高效的预警机制和临床干预-反馈机制,实现了感染全过程监测和感染预防控制“关口前移”,开创了医院感染监测与预防控制工作新模式.     

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.     

A spatial scan statistic for nonisotropic two-level risk cluster

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster.     

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.     

Review of syndromic surveillance: implications for waterborne disease detection

Syndromic surveillance is the gathering of data for public health purposes before laboratory or clinically confirmed information is available. Interest in syndromic surveillance has increased because of concerns about bioterrorism. In addition to bioterrorism detection, syndromic surveillance may be suited to detecting waterborne disease outbreaks. Theoretical benefits of syndromic surveillance include potential timeliness, increased response capacity, ability to establish baseline disease burdens, and ability to delineate the geographical reach of an outbreak. This review summarises the evidence gathered from retrospective, prospective, and simulation studies to assess the efficacy of syndromic surveillance for waterborne disease detection. There is little evidence that syndromic surveillance mitigates the effects of disease outbreaks through earlier detection and response. Syndromic surveillance should not be implemented at the expense of traditional disease surveillance, and should not be relied upon as a principal outbreak detection tool. The utility of syndromic surveillance is dependent on alarm thresholds that can be evaluated in practice. Syndromic data sources such as over the counter drug sales for detection of waterborne outbreaks should be further evaluated.     

中国传染病监测系统综合评价指标体系构建

  目的  对国内外新发（重大）传染病哨点的相关研究成果进行评述,明确哨点内涵的维度,进而提出建设传染病全哨点的深层蕴含,并对新发传染病“全哨点”进行概念界定,以期为这一主题的相关研究提供一些启示。  方法   运用内容分析法和共词聚类法对哨点的核心理念梳理并归类,使用逻辑“解构”的方法,将现有研究有关新发（重大）传染病“全哨点”的描述解析为概念的“主体”、“本质属性”、“监测目的”、“监测内容”、“分类”“分级”6个维度,构建“全哨点”的内涵与外延结构,最后运用形式逻辑定义法对“全哨点”进行概念界定。  结果  传染病哨点是以高危人群和重点人群为监测主体,以尽早发现并预警传染病、制定传染病防治策略和干预措施为目的,以人口学特征、行为学特征、流行病学特点、卫生服务干预、症状、体征、辅助检查等为主要监测内容的机构或者部门。  结论  新时代明确“何为新发（重大）传染病全哨点”意义重大,可以指导传染病哨点的理论和实践,对我国传染病哨点的建设具有指导意义。