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Syndromic surveillance: two decades experience of sustainable systems – its people not just data!
Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically con...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cambridge University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518508/ https://www.ncbi.nlm.nih.gov/pubmed/30869042 http://dx.doi.org/10.1017/S0950268819000074 |
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author | Smith, Gillian E. Elliot, Alex J. Lake, Iain Edeghere, Obaghe Morbey, Roger Catchpole, Mike Heymann, David L. Hawker, Jeremy Ibbotson, Sue McCloskey, Brian Pebody, Richard |
author_facet | Smith, Gillian E. Elliot, Alex J. Lake, Iain Edeghere, Obaghe Morbey, Roger Catchpole, Mike Heymann, David L. Hawker, Jeremy Ibbotson, Sue McCloskey, Brian Pebody, Richard |
author_sort | Smith, Gillian E. |
collection | PubMed |
description | Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heatwave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games).We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of ‘big data’, but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services. |
format | Online Article Text |
id | pubmed-6518508 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Cambridge University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-65185082019-06-04 Syndromic surveillance: two decades experience of sustainable systems – its people not just data! Smith, Gillian E. Elliot, Alex J. Lake, Iain Edeghere, Obaghe Morbey, Roger Catchpole, Mike Heymann, David L. Hawker, Jeremy Ibbotson, Sue McCloskey, Brian Pebody, Richard Epidemiol Infect Review Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heatwave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games).We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of ‘big data’, but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services. Cambridge University Press 2019-02-22 /pmc/articles/PMC6518508/ /pubmed/30869042 http://dx.doi.org/10.1017/S0950268819000074 Text en © Cambridge University Press 2019 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Smith, Gillian E. Elliot, Alex J. Lake, Iain Edeghere, Obaghe Morbey, Roger Catchpole, Mike Heymann, David L. Hawker, Jeremy Ibbotson, Sue McCloskey, Brian Pebody, Richard Syndromic surveillance: two decades experience of sustainable systems – its people not just data! |
title | Syndromic surveillance: two decades experience of sustainable systems – its people not just data! |
title_full | Syndromic surveillance: two decades experience of sustainable systems – its people not just data! |
title_fullStr | Syndromic surveillance: two decades experience of sustainable systems – its people not just data! |
title_full_unstemmed | Syndromic surveillance: two decades experience of sustainable systems – its people not just data! |
title_short | Syndromic surveillance: two decades experience of sustainable systems – its people not just data! |
title_sort | syndromic surveillance: two decades experience of sustainable systems – its people not just data! |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518508/ https://www.ncbi.nlm.nih.gov/pubmed/30869042 http://dx.doi.org/10.1017/S0950268819000074 |
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