Zheng Hongzhang, Woodall William H, Carlson Abigail L, DeLisle Sylvain
Department of Medicine, Veterans Affairs Maryland Health Care System, Baltimore, MD, United States of America.
School of Medicine, University of Maryland, Baltimore, MD, United States of America.
PLoS One. 2018 Jan 31;13(1):e0191324. doi: 10.1371/journal.pone.0191324. eCollection 2018.
As the deployment of electronic medical records (EMR) expands, so is the availability of long-term datasets that could serve to enhance public health surveillance. We hypothesized that EMR-based surveillance systems that incorporate seasonality and other long-term trends would discover outbreaks of acute respiratory infections (ARI) sooner than systems that only consider the recent past.
We simulated surveillance systems aimed at discovering modeled influenza outbreaks injected into backgrounds of patients with ARI. Backgrounds of daily case counts were either synthesized or obtained by applying one of three previously validated ARI case-detection algorithms to authentic EMR entries. From the time of outbreak injection, detection statistics were applied daily on paired background+injection and background-only time series. The relationship between the detection delay (the time from injection to the first alarm uniquely found in the background+injection data) and the false-alarm rate (FAR) was determined by systematically varying the statistical alarm threshold. We compared this relationship for outbreak detection methods that utilized either 7 days (early aberrancy reporting system (EARS)) or 2-4 years of past data (seasonal autoregressive integrated moving average (SARIMA) time series modeling).
In otherwise identical surveillance systems, SARIMA detected epidemics sooner than EARS at any FAR below 10%. The algorithms used to detect single ARI cases impacted both the feasibility and marginal benefits of SARIMA modeling. Under plausible real-world conditions, SARIMA could reduce detection delay by 5-16 days. It also was more sensitive at detecting the summer wave of the 2009 influenza pandemic.
Time series modeling of long-term historical EMR data can reduce the time it takes to discover epidemics of ARI. Realistic surveillance simulations may prove invaluable to optimize system design and tuning.
随着电子病历（EMR）的应用范围不断扩大，可用于加强公共卫生监测的长期数据集也越来越多。我们假设，纳入季节性和其他长期趋势的基于电子病历的监测系统，比仅考虑近期情况的系统能更快发现急性呼吸道感染（ARI）疫情。
我们模拟了旨在发现注入ARI患者背景中的模拟流感疫情的监测系统。每日病例数背景要么是合成的，要么是通过将三种先前验证过的ARI病例检测算法之一应用于真实的电子病历记录而获得的。从疫情注入之时起，每天对配对的背景+注入和仅背景时间序列应用检测统计数据。通过系统地改变统计警报阈值，确定检测延迟（从注入到在背景+注入数据中唯一发现的首次警报的时间）与误报率（FAR）之间的关系。我们比较了利用7天（早期异常报告系统（EARS））或过去2 - 4年数据（季节性自回归积分移动平均（SARIMA）时间序列建模）的疫情检测方法的这种关系。
在其他条件相同的监测系统中，在任何低于10%的误报率下，SARIMA比EARS能更快检测到疫情。用于检测单个ARI病例的算法影响了SARIMA建模的可行性和边际效益。在合理的现实条件下，SARIMA可将检测延迟缩短5 - 16天。它在检测2009年流感大流行的夏季波时也更敏感。
对长期历史电子病历数据进行时间序列建模可以减少发现ARI疫情所需的时间。现实的监测模拟可能对优化系统设计和调整非常有价值。
