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Syndromic surveillance: two decades experience of sustainable systems – its people not just data!

  • Gillian E. Smith (a1) (a2), Alex J. Elliot (a1) (a2), Iain Lake (a2) (a3), Obaghe Edeghere (a1) (a2) (a4), Roger Morbey (a1) (a2), Mike Catchpole (a5), David L. Heymann (a6), Jeremy Hawker (a4), Sue Ibbotson (a7), Brian McCloskey (a8), Richard Pebody (a9) and Public Health England Real-time Syndromic Surveillance Team (a1) (a2) (a3) (a4) (a5) (a6) (a7) (a8) (a9)...

Abstract

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.

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Copyright

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.

Corresponding author

Author for correspondence: Gillian E. Smith, E-mail: gillian.smith@phe.gov.uk

Footnotes

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Amardeep Bains, Sally Harcourt, Helen Hughes, Winnie Lee, Paul Loveridge, Sue Smith, Ana Soriano.

Footnotes

References

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1.Lazarus, R et al. (2001) Using automated medical records for rapid identification of illness syndromes (syndromic surveillance): the example of lower respiratory infection. BMC Public Health 1, 9. Available at https://doi.org/10.1186/1471-2458-1-9.
2.Heffernan, R et al. (2004) New York city syndromic surveillance systems. MMWR Supplements 53, 2327.
3.Steiner-Sichel, L et al. (2004) Field investigations of emergency department syndromic surveillance signals – New York city. MMWR Supplements 53, 184189.
4.Heffernan, R et al. (2004) Syndromic surveillance in public health practice, New York city. Emerging Infectious Diseases 10, 858864.
5.Morbey, RA et al. (2015) The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England. Bioinformatics (Oxford, England) 31, 36603665.
6.Morbey, RA et al. (2015) Development and refinement of new statistical methods for enhanced syndromic surveillance during the 2012 Olympic and Paralympic Games. Health Informatics Journal 21, 159169.
7.Smith, GE et al. (2017) Novel public health risk assessment process developed to support syndromic surveillance for the 2012 Olympic and Paralympic Games. Journal of Public Health 39, e111e117.
8.Smith, S et al. (2011) Early spread of the 2009 influenza A(H1N1) pandemic in the United Kingdom – use of local syndromic data, May–August 2009. Eurosurveillance 16, pii=19771. Available at http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=19771.
9.Hughes, HE et al. (2014) Using an emergency department syndromic surveillance system to investigate the impact of extreme cold weather events. Public Health 128, 628635.
10.Smith, GE et al. (2015) Using real-time syndromic surveillance systems to help explore the acute impact of the air pollution incident of March/April 2014 in England. Environmental Research, 136, 500504.
11.Smith, S et al. (2016) The impact of heatwaves on community morbidity and healthcare usage: a retrospective observational study using real-time syndromic surveillance. International Journal of Environmental Research and Public Health 13, Available at https://doi.org/10.3390/ijerph13010132.
12.Smith, S et al. (2016) Estimating the burden of heat illness in England during the 2013 summer heatwave using syndromic surveillance. Journal of Epidemiology and Community Health 70, 459465.
13.Todkill, D et al. (2016) An observational study using English syndromic surveillance data collected during the 2012 London Olympics – what did syndromic surveillance show and what can we learn for future mass-gathering events? Prehospital and Disaster Medicine 31, 628634.
14.McCloskey, B et al. (2014) London 2012 Olympic and Paralympic games: public health surveillance and epidemiology. Lancet 383, 20832089.
15.Bawa, Z et al. (2015) Assessing the likely impact of a rotavirus vaccination programme in England; the contribution of syndromic surveillance. Clinical Infectious Diseases 61, 7785.
16.Jafarpour, N et al. (2015) Quantifying the determinants of outbreak detection performance through simulation and machine learning. Journal of Biomedical Informatics 53, 180187.
17.Jafarpour, N et al. (2013) Using hierarchical mixture of experts model for fusion of outbreak detection methods. AMIA Annual Symposium Proceedings 2013, 663669.
18.Spreco, A et al. (2017) Integrated detection and prediction of influenza activity for real-time surveillance: algorithm design. Journal of Medical Internet Research 19, e211. doi: 210.2196/jmir.7101.
19.Unkel, S et al. (2012) Statistical methods for the prospective detection of infectious disease outbreaks: a review. Journal of the Royal Statistical Society Series A (Statistics in Society) 175, 4982.
20.Yan, P, Chen, H and Zeng, D (2008) Syndromic surveillance systems. Annual Review of Information Science and Technology, 42, 425495. Available at https://doi.org/10.1002/aris.2008.1440420117.
21.Elliot, AJ et al. (2012) Establishing an emergency department syndromic surveillance system to support the London 2012 Olympic and Paralympic Games. Emergency Medicine Journal 29, 954960.
22.Josseran, L et al. (2009) Syndromic surveillance and heat wave morbidity: a pilot study based on emergency departments in France. BMC Medical Informatics and Decision Making 9, 14. Available at https://doi.org/10.1186/1472-6947-9-14.
23.Triple, S (2011) Assessment of syndromic surveillance in Europe. Lancet 378, 18331834.

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