Time series modeling for syndromic surveillance.
Time series modeling for syndromic surveillance.
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DOI：
10.1186/1472-6947-3-2
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发表时间：
2003-01-23
影响因子：
3.5
通讯作者：
Mandl, Kenneth D
中科院分区：
文献类型：
--
作者：
Reis, Ben Y;Mandl, Kenneth D
背景技术背景：基于急诊科（艾德）的综合征监测系统识别出异常高的就诊率，这可能是生物恐怖袭击的早期信号。例如，炭疽病爆发可能首先是由于向艾德报告的呼吸道症状患者数量的异常增加而被发现的。可靠地识别这些异常的访问模式需要很好地了解医疗保健使用的正常模式。不幸的是，系统的方法来确定预期的数量（艾德）访问在特定的一天还没有很好地建立。我们在这里提出了一个广义的方法，用于开发模型的预期艾德访问rates.METHODS：使用时间序列方法，我们开发了强大的模型，艾德利用率的目的，定义预期的访问率。这些模型是基于一个主要的大都市学术三级护理儿科急诊科近十年的历史数据。历史数据采用截尾平均季节模型拟合，其他模型采用自回归积分移动平均（ARIMA）残差拟合，以说明数据的近期趋势。模型的检测能力进行了测试与模拟outbreaks.RESULTS：模型建立的整体访问和医疗相关的访问，根据在每次访问开始时记录的主诉分类。ARIMA模型的平均绝对百分比误差为9.37%（总体访视）和27.54%（呼吸访视）。一个简单的检测系统的基础上的ARIMA模型的总体访问是能够检测7天的模拟爆发，每天30次访问，100%的灵敏度和97%的特异性。敏感性下降与爆发规模，下降到94%的爆发，每天20次访问，每天10次访问的57%，同时保持97%的基准specificity.CONCLUSIONS：时间序列方法应用于历史艾德利用数据是一个重要的工具，症状监测。准确预测急诊科的总利用率以及特定综合征的发生率是可能的。系统中的多个模型说明了长期和近期趋势，结合这两个角度的综合警报策略可能会为公共卫生当局提供更完整的信息。这里描述的系统方法可以推广到其他医疗环境，以开发能够检测疾病模式和医疗利用异常的自动化监测系统。
BACKGROUND: Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates.METHODS: Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks.RESULTS: Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity.CONCLUSIONS: Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization.