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Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology

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  • Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology

    JMIR Public Health Surveill. 2018 Jul 6;4(3):e59. doi: 10.2196/publichealth.9876.
    Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.

    Meyers L1, Ginocchio CC1,2,3, Faucett AN1, Nolte FS4, Gesteland PH5, Leber A6, Janowiak D7, Donovan V8, Dien Bard J9,10, Spitzer S11, Stellrecht KA12, Salimnia H13, Selvarangan R14, Juretschko S15, Daly JA16, Wallentine JC17, Lindsey K18, Moore F18, Reed SL19, Aguero-Rosenfeld M20, Fey PD21, Storch GA22, Melnick SJ23, Robinson CC24, Meredith JF25, Cook CV1, Nelson RK1, Jones JD1, Scarpino SV26, Althouse BM27,28, Ririe KM29, Malin BA30, Poritz MA#31.
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    Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy.

    The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems.

    We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States.

    The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present.

    Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.
    ŠLindsay Meyers, Christine C Ginocchio, Aimie N Faucett, Frederick S Nolte, Per H Gesteland, Amy Leber, Diane Janowiak, Virginia Donovan, Jennifer Dien Bard, Silvia Spitzer, Kathleen A Stellrecht, Hossein Salimnia, Rangaraj Selvarangan, Stefan Juretschko, Judy A Daly, Jeremy C Wallentine, Kristy Lindsey, Franklin Moore, Sharon L Reed, Maria Aguero-Rosenfeld, Paul D Fey, Gregory A Storch, Steve J Melnick, Christine C Robinson, Jennifer F Meredith, Camille V Cook, Robert K Nelson, Jay D Jones, Samuel V Scarpino, Benjamin M Althouse, Kirk M Ririe, Bradley A Malin, Mark A Poritz. Originally published in JMIR Public Health and Surveillance (, 06.07.2018.


    communicable disease; epidemiology; internet; pathology, molecular; patients; privacy

    PMID: 29980501 DOI: 10.2196/publichealth.9876
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