Disease Surveillance in Real Time: Geotemporal Methods

实时疾病监测：地时方法

基本信息

批准号：
7908947
负责人：
KENNETH D MANDL
金额：
$ 18.25万
依托单位：
BOSTON CHILDREN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-30 至 2011-09-29
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant):Real time data collection and analysis would substantially strengthen public health surveillance at both regional and national levels. Although symptom and diagnostic data are stored in administrative health care databases, there currently are no automated systems for integrating this data so that abnormal patterns of disease can be detected in a timely manner, a problem highlighted by recent bioterrorist attacks. We aim to address this by working with an existing and rapidly expanding data acquisition infrastructure to develop the analytic tools needed to recognize disease clusters as soon as possible once patients begin appearing at health care sites. We have already established a surveillance network by virtually integrating multiple hospital emergency department (ED) databases in real time. This provides a picture of regional population patterns of disease. Furthermore, at one of these hospitals, we have assembled retrospective databases with many years of historical data critical for establishing normality for the analytic models. Thus, the goal of this project is to strengthen surveillance systems by using readily available information in realtime, thereby increasing the power of these systems to detect disease outbreaks sooner. Because of the urgent need to quickly identify victims of biowarfare agents such as anthrax, we will concentrate on identifying clusters of patients presenting with respiratory syndromes. The proposed study will first establish normal patterns of disease and then build models that enable the detection of deviations from these patterns with a minimum of false alarms. Once these methods are established, they can be applied to any other meaningful set of syndromes.

描述（由申请人提供）：实时数据收集和分析将大大加强区域和国家一级的公共卫生监视。尽管症状和诊断数据存储在行政卫生保健数据库中，但目前尚无自动化系统来整合该数据，因此可以及时检测到疾病的异常模式，这是最近生物恐怖主义攻击强调的问题。我们的目的是通过与现有且迅速扩展的数据采集基础设施合作来解决这一问题，以开发一旦患者开始出现在医疗保健站点，以尽快识别疾病簇所需的分析工具。我们已经通过实时整合多个医院急诊科（ED）数据库来建立一个监视网络。这提供了疾病的区域人口模式的图片。此外，在其中一家医院，我们组装了回顾性数据库，这些数据库的历史数据对于建立分析模型的正常性至关重要。因此，该项目的目的是通过实时使用随时可用的信息来增强监视系统，从而提高这些系统的力量以更快地检测疾病爆发。由于迫切需要快速识别诸如炭疽等生物果剂的受害者，因此我们将集中精力确定出现呼吸道综合征的患者簇。拟议的研究将首先建立正常的疾病模式，然后建立模型，以最少的错误警报来检测与这些模式的偏差。一旦建立了这些方法，它们就可以应用于任何其他有意义的综合症集。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Temporal patterns of medications dispensed to children and adolescents in a national insured population.

向全国参保人群中的儿童和青少年发放药物的时间模式。

DOI：
10.1371/journal.pone.0040991
发表时间：
2012
期刊：
PloS one
影响因子：
3.7
作者：
Olson,KarenL;Mandl,KennethD
通讯作者：
Mandl,KennethD

Time series modeling for syndromic surveillance.

DOI：
10.1186/1472-6947-3-2
发表时间：
2003-01-23
期刊：
BMC medical informatics and decision making
影响因子：
3.5
作者：
Reis, Ben Y;Mandl, Kenneth D
通讯作者：
Mandl, Kenneth D

Lumbar puncture ordering and results in the pediatric population: a promising data source for surveillance systems.

儿科人群的腰椎穿刺顺序和结果：监测系统的一个有前途的数据源。

DOI：
10.1197/j.aem.2006.02.009
发表时间：
2006
期刊：
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.
影响因子：
0
作者：
Kimia,Amir;Brownstein,JohnS;Olson,KarenL;Zak,Victor;Bourgeois,FlorenceT;Mandl,KennethD
通讯作者：
Mandl,KennethD

An epidemiological network model for disease outbreak detection.