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Robillard DW, Sundermann AJ, Raux BR, Prinzi AM. Navigating the network: a narrative overview of AMR surveillance and data flow in the United States. ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2024; 4:e55. [PMID: 38655022 PMCID: PMC11036423 DOI: 10.1017/ash.2024.64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/26/2024]
Abstract
The antimicrobial resistance (AMR) surveillance landscape in the United States consists of a data flow that starts in the clinical setting and is maintained by a network of national and state public health laboratories. These organizations are well established, with robust methodologies to test and confirm antimicrobial susceptibility. Still, the bridge that guides the flow of data is often one directional and caught in a constant state of rush hour that can only be refined with improvements to infrastructure and automation in the data flow. Moreover, there is an absence of information in the literature explaining the processes clinical laboratories use to coalesce and share susceptibility test data for AMR surveillance, further complicated by variability in testing procedures. This knowledge gap limits our understanding of what is needed to improve and streamline data sharing from clinical to public health laboratories. Successful models of AMR surveillance display attributes like 2-way communication between clinical and public health laboratories, centralized databases, standardized data, and the use of electronic health records or data systems, highlighting areas of opportunity and improvement. This article explores the roles and processes of the organizations involved in AMR surveillance in the United States and identifies current knowledge gaps and opportunities to improve communication between them through standardization, communication, and modernization of data flow.
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Affiliation(s)
- Darin W. Robillard
- Division of Public Health, University of Utah School of Medicine, Salt Lake City, UT, USA
- Corporate Program Management, bioMérieux, Salt Lake City, UT, USA
| | - Alexander J. Sundermann
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Brian R. Raux
- US Medical Affairs, bioMérieux, Salt Lake City, UT, USA
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Bouyer F, Thiongane O, Hobeika A, Arsevska E, Binot A, Corrèges D, Dub T, Mäkelä H, van Kleef E, Jori F, Lancelot R, Mercier A, Fagandini F, Valentin S, Van Bortel W, Ruault C. Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance. BMC Public Health 2024; 24:973. [PMID: 38582850 PMCID: PMC10999084 DOI: 10.1186/s12889-024-18466-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/27/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. METHODS We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. RESULTS Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. CONCLUSIONS The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.
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Affiliation(s)
- Fanny Bouyer
- Groupe d'Expérimentation et de Recherche: Développement et Actions Locales (GERDAL), Angers, France.
| | - Oumy Thiongane
- Joint Research Unit Animal, Health, Territories, Risks, Ecosystems (UMR ASTRE), French Agricultural Research Centre for International Development (CIRAD), National Research Institute for Agriculture, Food and Environment (INRAE), Montpellier, France
| | - Alexandre Hobeika
- UMR MOISA, French Agricultural Research Centre for International Development (CIRAD), 34398, Montpellier, France
- MOISA, University Montpellier, CIHEAM-IAMM, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Elena Arsevska
- Joint Research Unit Animal, Health, Territories, Risks, Ecosystems (UMR ASTRE), French Agricultural Research Centre for International Development (CIRAD), National Research Institute for Agriculture, Food and Environment (INRAE), Montpellier, France
| | - Aurélie Binot
- Joint Research Unit Animal, Health, Territories, Risks, Ecosystems (UMR ASTRE), French Agricultural Research Centre for International Development (CIRAD), National Research Institute for Agriculture, Food and Environment (INRAE), Montpellier, France
| | - Déborah Corrèges
- Joint Research Unit EPIdemiological On Animal and Zoonotic Diseases (UMR EPIA), National School of Veterinary Services (VetAgro Sup), National Research Institute for Agriculture, Food and Environment (INRAE), Marcy L'Etoile, France
| | - Timothée Dub
- Department of Health Security, Finish Institute for Health and Welfare, Helsinki, Finland
| | - Henna Mäkelä
- Department of Health Security, Finish Institute for Health and Welfare, Helsinki, Finland
| | - Esther van Kleef
- Institute of Tropical Medicine, Department of Biomedical Sciences, Outbreak Research Team, Antwerp, Belgium
| | - Ferran Jori
- Joint Research Unit Animal, Health, Territories, Risks, Ecosystems (UMR ASTRE), French Agricultural Research Centre for International Development (CIRAD), National Research Institute for Agriculture, Food and Environment (INRAE), Montpellier, France
| | - Renaud Lancelot
- Joint Research Unit Animal, Health, Territories, Risks, Ecosystems (UMR ASTRE), French Agricultural Research Centre for International Development (CIRAD), National Research Institute for Agriculture, Food and Environment (INRAE), Montpellier, France
| | - Alize Mercier
- Joint Research Unit Animal, Health, Territories, Risks, Ecosystems (UMR ASTRE), French Agricultural Research Centre for International Development (CIRAD), National Research Institute for Agriculture, Food and Environment (INRAE), Montpellier, France
| | - Francesca Fagandini
- Joint Research Unit Land, Remote Sensing and Spatial Information (UMR TETIS), French Agricultural Research Centre for International Development (CIRAD), Montpellier, France
| | - Sarah Valentin
- Joint Research Unit Land, Remote Sensing and Spatial Information (UMR TETIS), French Agricultural Research Centre for International Development (CIRAD), Montpellier, France
| | - Wim Van Bortel
- Institute of Tropical Medicine, Department of Biomedical Sciences, Outbreak Research Team, Antwerp, Belgium
- Institute of Tropical Medicine, Department of Biomedical Sciences, Unit of Entomology, Antwerp, Belgium
| | - Claire Ruault
- Groupe d'Expérimentation et de Recherche: Développement et Actions Locales (GERDAL), Angers, France
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Leal Neto O, Paolotti D, Dalton C, Carlson S, Susumpow P, Parker M, Phetra P, Lau EHY, Colizza V, Jan van Hoek A, Kjelsø C, Brownstein JS, Smolinski MS. Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View. JMIR Public Health Surveill 2023; 9:e46644. [PMID: 37490846 PMCID: PMC10504624 DOI: 10.2196/46644] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/21/2023] [Accepted: 07/25/2023] [Indexed: 07/27/2023] Open
Abstract
Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.
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Affiliation(s)
- Onicio Leal Neto
- Ending Pandemics, San Francisco, CA, United States
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | | | | | | | | | | | | | - Eric H Y Lau
- School of Public Health, University of Hong Kong, Hong Kong, China
| | - Vittoria Colizza
- Pierre Louis Institute of Epidemiology and Public Health, INSERM, Sorbonne Université, Paris, France
| | - Albert Jan van Hoek
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | | | - John S Brownstein
- Boston Children Hospital, Harvard University, Boston, MA, United States
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Scott P, Adedeji T, Nakkas H, Andrikopoulou E. One Health in a Digital World: Technology, Data, Information and Knowledge. Yearb Med Inform 2023; 32:10-18. [PMID: 37414034 PMCID: PMC10751116 DOI: 10.1055/s-0043-1768718] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023] Open
Abstract
OBJECTIVES To describe the origins and growth of the One Health concept and its recent application in One Digital Health. METHODS Bibliometric review and critical discussion of emergent themes derived from co-occurrence of MeSH keywords. RESULTS The fundamental interrelationship between human health, animal health and the wider environment has been recognized since ancient times. One Health as a distinct term originated in 2004 and has been a rapidly growing concept of interest in the biomedical literature since 2017. One Digital Health has quickly established itself as a unifying construct that highlights the critical role of technology, data, information and knowledge to facilitate the interdisciplinary collaboration that One Health requires. The principal application domains of One Digital Health to date are in FAIR data integration and analysis, disease surveillance, antimicrobial stewardship and environmental monitoring. CONCLUSIONS One Health and One Digital Health offer powerful lenses to examine and address crises in our living world. We propose thinking in terms of Learning One Health Systems that can dynamically capture, integrate, analyse and monitor application of data across the biosphere.
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Affiliation(s)
- Philip Scott
- Institute of Management & Health, University of Wales Trinity Saint David, Swansea, Wales, UK
| | - Taiwo Adedeji
- School of Computing, University of Portsmouth, Portsmouth, UK
| | - Haythem Nakkas
- School of Computing, University of Portsmouth, Portsmouth, UK
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Kim PY, Kim AY, Newman JJ, Cella E, Bishop TC, Huwe PJ, Uchakina ON, McKallip RJ, Mack VL, Hill MP, Ogungbe IV, Adeyinka O, Jones S, Ware G, Carroll J, Sawyer JF, Densmore KH, Foster M, Valmond L, Thomas J, Azarian T, Queen K, Kamil JP. A collaborative approach to improving representation in viral genomic surveillance. PLOS GLOBAL PUBLIC HEALTH 2023; 3:e0001935. [PMID: 37467165 PMCID: PMC10355392 DOI: 10.1371/journal.pgph.0001935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/05/2023] [Indexed: 07/21/2023]
Abstract
The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building 'next generation' genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness.
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Affiliation(s)
- Paul Y. Kim
- Department of Biological Sciences, Grambling State University, Grambling, LA, United States of America
| | - Audrey Y. Kim
- Department of Biological Sciences, Grambling State University, Grambling, LA, United States of America
| | - Jamie J. Newman
- School of Biological Sciences, Louisiana Tech University, Ruston, LA, United States of America
| | - Eleonora Cella
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, United States of America
| | - Thomas C. Bishop
- Physics and Chemistry Programs, Louisiana Tech University, Ruston, LA, United States of America
| | - Peter J. Huwe
- Mercer University School of Medicine, Macon, GA, United States of America
| | - Olga N. Uchakina
- Mercer University School of Medicine, Macon, GA, United States of America
| | - Robert J. McKallip
- Mercer University School of Medicine, Macon, GA, United States of America
| | - Vance L. Mack
- Mercer Medicine, Macon, GA, United States of America
| | | | - Ifedayo Victor Ogungbe
- Department of Chemistry, Jackson State University, Jackson, MS, United States of America
| | - Olawale Adeyinka
- Department of Chemistry, Jackson State University, Jackson, MS, United States of America
| | - Samuel Jones
- Health Services Center, Jackson State University, Jackson, MS, United States of America
| | - Gregory Ware
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Jennifer Carroll
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Jarrod F. Sawyer
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Kenneth H. Densmore
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Michael Foster
- School of Biological Sciences, Louisiana Tech University, Ruston, LA, United States of America
| | - Lescia Valmond
- Department of Biological Sciences, Grambling State University, Grambling, LA, United States of America
| | - John Thomas
- Department of Biological Sciences, Grambling State University, Grambling, LA, United States of America
| | - Taj Azarian
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, United States of America
| | - Krista Queen
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
| | - Jeremy P. Kamil
- Center of Excellence for Emerging Viral Threats, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
- Department of Microbiology and Immunology, Louisiana State University Health Shreveport, Shreveport, LA, United States of America
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