TY - JOUR
T1 - Automated real-time collection of pathogen-specific diagnostic data
T2 - Syndromic infectious disease epidemiology
AU - Meyers, Lindsay
AU - Ginocchio, Christine C.
AU - Faucett, Aimie N.
AU - Nolte, Frederick S.
AU - Gesteland, Per H.
AU - Leber, Amy
AU - Janowiak, Diane
AU - Donovan, Virginia
AU - Bard, Jennifer Dien
AU - Spitzer, Silvia
AU - Stellrecht, Kathleen A.
AU - Salimnia, Hossein
AU - Selvarangan, Rangaraj
AU - Juretschko, Stefan
AU - Daly, Judy A.
AU - Wallentine, Jeremy C.
AU - Lindsey, Kristy
AU - Moore, Franklin
AU - Reed, Sharon L.
AU - Aguero-Rosenfeld, Maria
AU - Fey, Paul D.
AU - Storch, Gregory A.
AU - Melnick, Steve J.
AU - Robinson, Christine C.
AU - Meredith, Jennifer F.
AU - Cook, Camille V.
AU - Nelson, Robert K.
AU - Jones, Jay D.
AU - Scarpino, Samuel V.
AU - Althouse, Benjamin M.
AU - Ririe, Kirk M.
AU - Malin, Bradley A.
AU - Poritz, Mark A.
N1 - Funding Information:
This work was partially supported by NIH grant 5U01AI074419 (LM, KMR, and MAP). The authors would like to thank Chris Thurston and Spencer Rose (BioFire Defense) for building the Trend public website; Andrew Wallin (BioFire Defense) for reviewing the MIE data analysis; Anna Hoffee (BioFire Diagnostics) for assistance with the figures; Mark Pallansch (CDC), Kirsten St. George (New York State Department of Health), and Allyn Nakashima (Utah Department of Health) for useful discussions; Anne Blaschke and colleagues at BioFire Diagnostics and BioFire Defense for reviewing the manuscript.
Publisher Copyright:
© Lindsay Meyers, Christine C Ginocchio, Aimie N Faucett, Frederick S Nolte, Per H Gesteland, Amy Leber, Diane Janowiak,.
PY - 2018/7
Y1 - 2018/7
N2 - Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.
AB - Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.
KW - Communicable disease
KW - Epidemiology
KW - Internet
KW - Pathology, molecular
KW - Patients
KW - Privacy
UR - http://www.scopus.com/inward/record.url?scp=85052019563&partnerID=8YFLogxK
U2 - 10.2196/publichealth.9876
DO - 10.2196/publichealth.9876
M3 - Article
C2 - 29980501
AN - SCOPUS:85052019563
SN - 2369-2960
VL - 4
JO - JMIR Public Health and Surveillance
JF - JMIR Public Health and Surveillance
IS - 7
M1 - e59
ER -