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    Can long-term historical data from electronic medical records improve surveillance for epidemics of acute respiratory infections? A systematic evaluation

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    Author
    Zheng, H.
    Woodall, W.H.
    Carlson, A.L.
    Date
    2018
    Journal
    PLoS ONE
    Publisher
    Public Library of Science
    Type
    Article
    
    Metadata
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    See at
    https://dx.doi.org/10.1371/journal.pone.0191324
    Abstract
    Background 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. Methods 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). Results 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. Conclusion 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. Copyright This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
    Keyword
    Acute Disease
    Electronic Health Records
    Epidemiological Monitoring
    Humans
    Influenza, Human
    Pandemics
    Respiratory Tract Infections
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041181881&doi=10.1371%2fjournal.pone.0191324&partnerID=40&md5=04f83b69363e2309576e7a01a4fde97b; http://hdl.handle.net/10713/9038
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pone.0191324
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