Early detection of influenza outbreaks using the DC Department of Health's syndromic surveillance system
Early detection of influenza outbreaks using the DC Department of Health's syndromic surveillance system
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DOI：
10.1186/1471-2458-9-483
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发表时间：
2009-12-22
影响因子：
4.5
通讯作者：
Stoto, Michael A.
中科院分区：
文献类型：
--
作者：
Griffin, Beth Ann;Jain, Arvind K.;Stoto, Michael A.
背景资料：自2001年以来，哥伦比亚特区卫生部一直在使用急诊室综合征监测系统，以确定可能的疾病爆发。从许多当地医院急诊室接收数据，并使用各种统计检测算法每天进行分析。本文的目的是描述这些统计检测算法在严格但实用的条件下的性能，以确定每个算法的最佳参数，并比较两种综合征定义标准和儿童医院与其他医院确定季节性流感发作的数据的能力。方法：我们首先使用微调的方法来提高每个算法的灵敏度，以检测模拟的爆发和识别以前已知的爆发。随后，使用微调算法，我们检查了（i）未指明的感染和呼吸综合征类别检测流感季节开始的能力，以及（ii）当使用未指明的感染、呼吸系统和两个类别时，来自儿童国家医疗中心（CNMC）的数据与所有其他医院的数据相比有多好。结果：使用数据的模拟研究表明，在一系列的情况下，多变量CRAMUM算法比其他算法测试更有效地执行。此外，每个算法产生最佳性能的参数各不相同，特别是随着数据流中的病例数而变化。在检测季节性流感的发病方面，只有“未指明的感染”，特别是来自CNMC的计数，在八种可用的综合征分类中清楚地描述了流感爆发。在三个五年，CNMC一贯标志早（从2天到2周前）比所有其他DC hospital.Conclusions多变量分析：当从业者应用统计检测算法，以自己的数据，微调参数是必要的，以提高整体灵敏度。通过微调算法，我们的研究结果表明，基于急诊室的症状监测侧重于儿童中未指明的感染病例，是确定流感爆发开始的有效方法，可以作为更密集监测工作的触发器，并在社区中启动感染控制措施。
Background: Since 2001, the District of Columbia Department of Health has been using an emergency room syndromic surveillance system to identify possible disease outbreaks. Data are received from a number of local hospital emergency rooms and analyzed daily using a variety of statistical detection algorithms. The aims of this paper are to characterize the performance of these statistical detection algorithms in rigorous yet practical terms in order to identify the optimal parameters for each and to compare the ability of two syndrome definition criteria and data from a children's hospital versus vs. other hospitals to determine the onset of seasonal influenza.Methods: We first used a fine-tuning approach to improve the sensitivity of each algorithm to detecting simulated outbreaks and to identifying previously known outbreaks. Subsequently, using the fine-tuned algorithms, we examined (i) the ability of unspecified infection and respiratory syndrome categories to detect the start of the flu season and (ii) how well data from Children's National Medical Center (CNMC) did versus all the other hospitals when using unspecified infection, respiratory, and both categories together.Results: Simulation studies using the data showed that over a range of situations, the multivariate CUSUM algorithm performed more effectively than the other algorithms tested. In addition, the parameters that yielded optimal performance varied for each algorithm, especially with the number of cases in the data stream. In terms of detecting the onset of seasonal influenza, only "unspecified infection," especially the counts from CNMC, clearly delineated influenza outbreaks out of the eight available syndromic classifications. In three of five years, CNMC consistently flags earlier (from 2 days up to 2 weeks earlier) than a multivariate analysis of all other DC hospitals.Conclusions: When practitioners apply statistical detection algorithms to their own data, fine tuning of parameters is necessary to improve overall sensitivity. With fined tuned algorithms, our results suggest that emergency room based syndromic surveillance focusing on unspecified infection cases in children is an effective way to determine the beginning of the influenza outbreak and could serve as a trigger for more intensive surveillance efforts and initiate infection control measures in the community.