Automated real-time collection of pathogen-specific diagnostic data: Syndromic infectious disease epidemiology

Lindsay 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-RosenfeldPaul 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

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article numbere59
JournalJournal of Medical Internet Research
Volume20
Issue number7
DOIs
StatePublished - Jul 2018

Fingerprint

Communicable Diseases
Epidemiology
Disease Outbreaks
Privacy
Confidentiality
Databases
Rhinovirus
Molecular Pathology
Ownership
Standard of Care
Coinfection
Routine Diagnostic Tests
Adenoviridae
Human Influenza
Parasites
Research Design
Public Health
Viruses
Bacteria
Delivery of Health Care

Keywords

  • Communicable disease
  • Epidemiology
  • Internet
  • Pathology, molecular
  • Patients
  • Privacy

ASJC Scopus subject areas

  • Health Informatics

Cite this

Meyers, L., Ginocchio, C. C., Faucett, A. N., Nolte, F. S., Gesteland, P. H., Leber, A., ... Poritz, M. A. (2018). Automated real-time collection of pathogen-specific diagnostic data: Syndromic infectious disease epidemiology. Journal of Medical Internet Research, 20(7), [e59]. https://doi.org/10.2196/publichealth.9876

Automated real-time collection of pathogen-specific diagnostic data : Syndromic infectious disease epidemiology. / Meyers, Lindsay; Ginocchio, Christine C.; Faucett, Aimie N.; Nolte, Frederick S.; Gesteland, Per H.; Leber, Amy; Janowiak, Diane; Donovan, Virginia; Bard, Jennifer Dien; Spitzer, Silvia; Stellrecht, Kathleen A.; Salimnia, Hossein; Selvarangan, Rangaraj; Juretschko, Stefan; Daly, Judy A.; Wallentine, Jeremy C.; Lindsey, Kristy; Moore, Franklin; Reed, Sharon L.; Aguero-Rosenfeld, Maria; Fey, Paul D; Storch, Gregory A.; Melnick, Steve J.; Robinson, Christine C.; Meredith, Jennifer F.; Cook, Camille V.; Nelson, Robert K.; Jones, Jay D.; Scarpino, Samuel V.; Althouse, Benjamin M.; Ririe, Kirk M.; Malin, Bradley A.; Poritz, Mark A.

In: Journal of Medical Internet Research, Vol. 20, No. 7, e59, 07.2018.

Research output: Contribution to journalArticle

Meyers, L, Ginocchio, CC, Faucett, AN, Nolte, FS, Gesteland, PH, Leber, A, Janowiak, D, Donovan, V, Bard, JD, Spitzer, S, Stellrecht, KA, Salimnia, H, Selvarangan, R, Juretschko, S, Daly, JA, Wallentine, JC, Lindsey, K, Moore, F, Reed, SL, Aguero-Rosenfeld, M, Fey, PD, Storch, GA, Melnick, SJ, Robinson, CC, Meredith, JF, Cook, CV, Nelson, RK, Jones, JD, Scarpino, SV, Althouse, BM, Ririe, KM, Malin, BA & Poritz, MA 2018, 'Automated real-time collection of pathogen-specific diagnostic data: Syndromic infectious disease epidemiology', Journal of Medical Internet Research, vol. 20, no. 7, e59. https://doi.org/10.2196/publichealth.9876
Meyers, Lindsay ; Ginocchio, Christine C. ; Faucett, Aimie N. ; Nolte, Frederick S. ; Gesteland, Per H. ; Leber, Amy ; Janowiak, Diane ; Donovan, Virginia ; Bard, Jennifer Dien ; Spitzer, Silvia ; Stellrecht, Kathleen A. ; Salimnia, Hossein ; Selvarangan, Rangaraj ; Juretschko, Stefan ; Daly, Judy A. ; Wallentine, Jeremy C. ; Lindsey, Kristy ; Moore, Franklin ; Reed, Sharon L. ; Aguero-Rosenfeld, Maria ; Fey, Paul D ; Storch, Gregory A. ; Melnick, Steve J. ; Robinson, Christine C. ; Meredith, Jennifer F. ; Cook, Camille V. ; Nelson, Robert K. ; Jones, Jay D. ; Scarpino, Samuel V. ; Althouse, Benjamin M. ; Ririe, Kirk M. ; Malin, Bradley A. ; Poritz, Mark A. / Automated real-time collection of pathogen-specific diagnostic data : Syndromic infectious disease epidemiology. In: Journal of Medical Internet Research. 2018 ; Vol. 20, No. 7.
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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.

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.

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KW - Epidemiology

KW - Internet

KW - Pathology, molecular

KW - Patients

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