Abstract
BackgroundHealth 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.ObjectiveThe 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.MethodsWe 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.ResultsThe 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.ConclusionsSyndromic 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.
Highlights
Surveillance LandscapeThe availability of real-time surveillance data that can monitor the spread of infectious diseases benefits public health [1,2,3]
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
Data from all commercially available FilmArray panels [45] are exported to the Trend database, we focus here on the Respiratory Panel (RP) that can detect 17 viral and three bacterial (Bordetella pertussis, Chlamydia pneumoniae, and Mycoplasma pneumoniae) pathogens [32,46,47]
Summary
Surveillance LandscapeThe availability of real-time surveillance data that can monitor the spread of infectious diseases benefits public health [1,2,3]. Numerous generation, syndromic surveillance systems, for example, pharmacy sales records [15,16], Twitter conversations [17,18], and Wikipedia hits [19,20] have come online in the past 5 years These systems cannot report the specific pathogen causing an increase in a particular set of symptoms. There are more localized efforts such as GermWatch in Utah [21] and the Electronic Clinical Laboratory Reporting System (ECLRS) in New York [22] that draw from hospital information systems (HISs) and laboratory information systems (LISs) This disparity in technologies and data collection methods results in incomplete surveillance. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy
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