Georgetown University Department of Health Systems Administration, Washington, DC 20007, USA.
BMC Public Health. 2009 Dec 22;9:483. doi: 10.1186/1471-2458-9-483.
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.
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.
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.
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.
自 2001 年以来，哥伦比亚特区卫生局一直使用急诊室综合征监测系统来识别可能的疾病暴发。数据来自多家当地医院的急诊室，每天使用各种统计检测算法进行分析。本文的目的是用严格而实用的方式来描述这些统计检测算法的性能，以确定每种算法的最佳参数，并比较两种综合征定义标准和儿童医院数据与其他医院数据的能力，以确定季节性流感的发病。
我们首先使用微调方法来提高每种算法检测模拟暴发和识别已知暴发的灵敏度。随后，使用微调算法，我们检查了（i）未指定感染和呼吸道综合征类别检测流感季节开始的能力，以及（ii）当使用未指定感染、呼吸道和两者类别时，儿童国家医学中心 (CNMC) 的数据与所有其他医院相比的表现。
使用数据进行的模拟研究表明，在一系列情况下，多变量 CUSUM 算法的性能优于测试的其他算法。此外，对于每种算法，产生最佳性能的参数因情况而异，尤其是数据流中的病例数。就检测季节性流感的发病而言，只有“未指定感染”，尤其是来自 CNMC 的计数，才能清楚地区分八种可用综合征分类中的流感暴发。在五年中的三年中，CNMC 始终比对所有其他 DC 医院的多元分析更早（提前 2 天至 2 周）发出流感爆发的信号。
当从业者将统计检测算法应用于自己的数据时，需要对参数进行微调以提高整体灵敏度。使用微调算法，我们的结果表明，以儿童未指定感染病例为重点的基于急诊室的综合征监测是确定流感暴发开始的有效方法，可作为加强监测努力和在社区启动感染控制措施的触发因素。
