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Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018

BACKGROUND: Timely reporting of microbiology test results is essential for infection management. Automated, machine-to-machine (M2M) reporting of diagnostic and antimicrobial resistance (AMR) data from laboratory information management systems (LIMS) to public health agencies improves timeliness and...

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Autores principales: Leitmeyer, Katrin Claire, Espinosa, Laura, Broberg, Eeva Kaarina, Struelens, Marc Jean, Allerberger, Franz, Dupont, Yves, Van Gucht, Steven, Quoilin, Sophie, Christova, Iva, Hunjak, Blazenka, Karagiannis, Christos, Křížová, Pavla, Částková, Jitka, Møller Nielsen, Eva, Kähler, Jonas, Peetso, Rita, Salmenlinna, Saara, Möttönen, Teemu, Coignard, Bruno, Diercke, Michaela, Vatopoulos, Alkiviadis, Tóth, Ákos, Kristinsson, Karl Gustaf, McNamara, Eleanor, Pantosi, Annalisa, Mavcutko, Violeta, Griškevičius, Algirdas, Mossong, Joël, Barbara, Christopher, Kortbeek, Titia, Caugant, Dominique, Dahle, Ulf, Vold, Line, Skoczyńska, Anna, Machado, Jorge, Ionescu, Gabriel, Madarova, Lucia, Avdičová, Mária, Paragi, Metka, Moreno Vazquez, Julio, Tegmark Wisell, Karin, Zambon, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: European Centre for Disease Prevention and Control (ECDC) 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531069/
https://www.ncbi.nlm.nih.gov/pubmed/33006301
http://dx.doi.org/10.2807/1560-7917.ES.2020.25.39.1900591
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author Leitmeyer, Katrin Claire
Espinosa, Laura
Broberg, Eeva Kaarina
Struelens, Marc Jean
Allerberger, Franz
Dupont, Yves
Van Gucht, Steven
Quoilin, Sophie
Christova, Iva
Hunjak, Blazenka
Karagiannis, Christos
Křížová, Pavla
Částková, Jitka
Møller Nielsen, Eva
Kähler, Jonas
Peetso, Rita
Salmenlinna, Saara
Möttönen, Teemu
Coignard, Bruno
Diercke, Michaela
Vatopoulos, Alkiviadis
Tóth, Ákos
Kristinsson, Karl Gustaf
McNamara, Eleanor
Pantosi, Annalisa
Mavcutko, Violeta
Griškevičius, Algirdas
Mossong, Joël
Barbara, Christopher
Kortbeek, Titia
Caugant, Dominique
Dahle, Ulf
Vold, Line
Skoczyńska, Anna
Machado, Jorge
Ionescu, Gabriel
Madarova, Lucia
Avdičová, Mária
Paragi, Metka
Moreno Vazquez, Julio
Tegmark Wisell, Karin
Zambon, Maria
author_facet Leitmeyer, Katrin Claire
Espinosa, Laura
Broberg, Eeva Kaarina
Struelens, Marc Jean
Allerberger, Franz
Dupont, Yves
Van Gucht, Steven
Quoilin, Sophie
Christova, Iva
Hunjak, Blazenka
Karagiannis, Christos
Křížová, Pavla
Částková, Jitka
Møller Nielsen, Eva
Kähler, Jonas
Peetso, Rita
Salmenlinna, Saara
Möttönen, Teemu
Coignard, Bruno
Diercke, Michaela
Vatopoulos, Alkiviadis
Tóth, Ákos
Kristinsson, Karl Gustaf
McNamara, Eleanor
Pantosi, Annalisa
Mavcutko, Violeta
Griškevičius, Algirdas
Mossong, Joël
Barbara, Christopher
Kortbeek, Titia
Caugant, Dominique
Dahle, Ulf
Vold, Line
Skoczyńska, Anna
Machado, Jorge
Ionescu, Gabriel
Madarova, Lucia
Avdičová, Mária
Paragi, Metka
Moreno Vazquez, Julio
Tegmark Wisell, Karin
Zambon, Maria
author_sort Leitmeyer, Katrin Claire
collection PubMed
description BACKGROUND: Timely reporting of microbiology test results is essential for infection management. Automated, machine-to-machine (M2M) reporting of diagnostic and antimicrobial resistance (AMR) data from laboratory information management systems (LIMS) to public health agencies improves timeliness and completeness of communicable disease surveillance. AIM: We surveyed microbiology data reporting practices for national surveillance of EU-notifiable diseases in European Union/European Economic Area (EU/EEA) countries in 2018. METHODS: European Centre for Disease Prevention and Control (ECDC) National Microbiology and Surveillance Focal Points completed a questionnaire on the modalities and scope of clinical microbiology laboratory data reporting. RESULTS: Complete data were provided for all 30 EU/EEA countries. Clinical laboratories used a LIMS in 28 countries. LIMS data on EU-notifiable diseases and AMR were M2M-reported to the national level in 14 and nine countries, respectively. In the 14 countries, associated demographic data reported allowed the de-duplication of patient reports. In 13 countries, M2M-reported data were used for cluster detection at the national level. M2M laboratory data reporting had been validated against conventional surveillance methods in six countries, and replaced those in five. Barriers to M2M reporting included lack of information technology support and financial incentives. CONCLUSION: M2M-reported laboratory data were used for national public health surveillance and alert purposes in nearly half of the EU/EEA countries in 2018. Reported data on infectious diseases and AMR varied in extent and disease coverage across countries and laboratories. Improving automated laboratory-based surveillance will depend on financial and regulatory incentives, and harmonisation of health information and communication systems.
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spelling pubmed-75310692020-10-13 Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018 Leitmeyer, Katrin Claire Espinosa, Laura Broberg, Eeva Kaarina Struelens, Marc Jean Allerberger, Franz Dupont, Yves Van Gucht, Steven Quoilin, Sophie Christova, Iva Hunjak, Blazenka Karagiannis, Christos Křížová, Pavla Částková, Jitka Møller Nielsen, Eva Kähler, Jonas Peetso, Rita Salmenlinna, Saara Möttönen, Teemu Coignard, Bruno Diercke, Michaela Vatopoulos, Alkiviadis Tóth, Ákos Kristinsson, Karl Gustaf McNamara, Eleanor Pantosi, Annalisa Mavcutko, Violeta Griškevičius, Algirdas Mossong, Joël Barbara, Christopher Kortbeek, Titia Caugant, Dominique Dahle, Ulf Vold, Line Skoczyńska, Anna Machado, Jorge Ionescu, Gabriel Madarova, Lucia Avdičová, Mária Paragi, Metka Moreno Vazquez, Julio Tegmark Wisell, Karin Zambon, Maria Euro Surveill Surveillance BACKGROUND: Timely reporting of microbiology test results is essential for infection management. Automated, machine-to-machine (M2M) reporting of diagnostic and antimicrobial resistance (AMR) data from laboratory information management systems (LIMS) to public health agencies improves timeliness and completeness of communicable disease surveillance. AIM: We surveyed microbiology data reporting practices for national surveillance of EU-notifiable diseases in European Union/European Economic Area (EU/EEA) countries in 2018. METHODS: European Centre for Disease Prevention and Control (ECDC) National Microbiology and Surveillance Focal Points completed a questionnaire on the modalities and scope of clinical microbiology laboratory data reporting. RESULTS: Complete data were provided for all 30 EU/EEA countries. Clinical laboratories used a LIMS in 28 countries. LIMS data on EU-notifiable diseases and AMR were M2M-reported to the national level in 14 and nine countries, respectively. In the 14 countries, associated demographic data reported allowed the de-duplication of patient reports. In 13 countries, M2M-reported data were used for cluster detection at the national level. M2M laboratory data reporting had been validated against conventional surveillance methods in six countries, and replaced those in five. Barriers to M2M reporting included lack of information technology support and financial incentives. CONCLUSION: M2M-reported laboratory data were used for national public health surveillance and alert purposes in nearly half of the EU/EEA countries in 2018. Reported data on infectious diseases and AMR varied in extent and disease coverage across countries and laboratories. Improving automated laboratory-based surveillance will depend on financial and regulatory incentives, and harmonisation of health information and communication systems. European Centre for Disease Prevention and Control (ECDC) 2020-10-01 /pmc/articles/PMC7531069/ /pubmed/33006301 http://dx.doi.org/10.2807/1560-7917.ES.2020.25.39.1900591 Text en This article is copyright of the authors or their affiliated institutions, 2020. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.
spellingShingle Surveillance
Leitmeyer, Katrin Claire
Espinosa, Laura
Broberg, Eeva Kaarina
Struelens, Marc Jean
Allerberger, Franz
Dupont, Yves
Van Gucht, Steven
Quoilin, Sophie
Christova, Iva
Hunjak, Blazenka
Karagiannis, Christos
Křížová, Pavla
Částková, Jitka
Møller Nielsen, Eva
Kähler, Jonas
Peetso, Rita
Salmenlinna, Saara
Möttönen, Teemu
Coignard, Bruno
Diercke, Michaela
Vatopoulos, Alkiviadis
Tóth, Ákos
Kristinsson, Karl Gustaf
McNamara, Eleanor
Pantosi, Annalisa
Mavcutko, Violeta
Griškevičius, Algirdas
Mossong, Joël
Barbara, Christopher
Kortbeek, Titia
Caugant, Dominique
Dahle, Ulf
Vold, Line
Skoczyńska, Anna
Machado, Jorge
Ionescu, Gabriel
Madarova, Lucia
Avdičová, Mária
Paragi, Metka
Moreno Vazquez, Julio
Tegmark Wisell, Karin
Zambon, Maria
Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018
title Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018
title_full Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018
title_fullStr Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018
title_full_unstemmed Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018
title_short Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018
title_sort automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a eu/eea survey, 2018
topic Surveillance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7531069/
https://www.ncbi.nlm.nih.gov/pubmed/33006301
http://dx.doi.org/10.2807/1560-7917.ES.2020.25.39.1900591
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