Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-23T16:14:50.192Z Has data issue: false hasContentIssue false
  • English
  • Français

Utility of Ambulance Data for Real-Time Syndromic Surveillance: A Pilot in the West Midlands Region, United Kingdom

Published online by Cambridge University Press:  01 August 2017

Dan Todkill ,
Paul Loveridge ,
Alex J. Elliot ,
Roger A. Morbey ,
Obaghe Edeghere ,
Tracy Rayment-Bishop ,
Chris Rayment-Bishop ,
John E. Thornes  and
Gillian Smith
Dan Todkill*
Affiliation:
Public Health England, Field Epidemiology Training Programme Fellow, United Kingdom Public Health England, Field Epidemiology Service West Midlands Office, National Infection Service, United Kingdom
Paul Loveridge
Affiliation:
Public Health England, Real-Time Syndromic Surveillance Team, National Infection Service, United Kingdom
Alex J. Elliot
Affiliation:
Public Health England, Real-Time Syndromic Surveillance Team, National Infection Service, United Kingdom
Roger A. Morbey
Affiliation:
Public Health England, Real-Time Syndromic Surveillance Team, National Infection Service, United Kingdom
Obaghe Edeghere
Affiliation:
Public Health England, Field Epidemiology Service West Midlands Office, National Infection Service, United Kingdom Public Health England, Real-Time Syndromic Surveillance Team, National Infection Service, United Kingdom
Tracy Rayment-Bishop
Affiliation:
West Midlands Ambulance Service NHS Foundation Trust, West Midlands, United Kingdom
Chris Rayment-Bishop
Affiliation:
West Midlands Ambulance Service NHS Foundation Trust, West Midlands, United Kingdom
John E. Thornes
Affiliation:
Centre for Radiation, Chemical and Environmental Hazards, Public Health England, Chilton, United Kingdom School of Health and Population Science, University of Birmingham, United Kingdom
Gillian Smith
Affiliation:
Public Health England, Real-Time Syndromic Surveillance Team, National Infection Service, United Kingdom
*
Correspondence: Dan Todkill, MFPH Public Health England, United Kingdom E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction

The Public Health England (PHE; United Kingdom) Real-Time Syndromic Surveillance Team (ReSST) currently operates four national syndromic surveillance systems, including an emergency department system. A system based on ambulance data might provide an additional measure of the “severe” end of the clinical disease spectrum. This report describes the findings and lessons learned from the development and preliminary assessment of a pilot syndromic surveillance system using ambulance data from the West Midlands (WM) region in England.

Hypothesis/Problem

Is an Ambulance Data Syndromic Surveillance System (ADSSS) feasible and of utility in enhancing the existing suite of PHE syndromic surveillance systems?

Methods

An ADSSS was designed, implemented, and a pilot conducted from September 1, 2015 through March 1, 2016. Surveillance cases were defined as calls to the West Midlands Ambulance Service (WMAS) regarding patients who were assigned any of 11 specified chief presenting complaints (CPCs) during the pilot period. The WMAS collected anonymized data on cases and transferred the dataset daily to ReSST, which contained anonymized information on patients’ demographics, partial postcode of patients’ location, and CPC. The 11 CPCs covered a broad range of syndromes. The dataset was analyzed descriptively each week to determine trends and key epidemiological characteristics of patients, and an automated statistical algorithm was employed daily to detect higher than expected number of calls. A preliminary assessment was undertaken to assess the feasibility, utility (including quality of key indicators), and timeliness of the system for syndromic surveillance purposes. Lessons learned and challenges were identified and recorded during the design and implementation of the system.

Results

The pilot ADSSS collected 207,331 records of individual ambulance calls (daily mean=1,133; range=923-1,350). The ADSSS was found to be timely in detecting seasonal changes in patterns of respiratory infections and increases in case numbers during seasonal events.

Conclusions

Further validation is necessary; however, the findings from the assessment of the pilot ADSSS suggest that selected, but not all, ambulance indicators appear to have some utility for syndromic surveillance purposes in England. There are certain challenges that need to be addressed when designing and implementing similar systems.

TodkillD, LoveridgeP, ElliotAJ, MorbeyRA, EdeghereO, Rayment-BishopT, Rayment-BishopC, ThornesJE, SmithG. Utility of Ambulance Data for Real-Time Syndromic Surveillance: A Pilot in the West Midlands Region, United Kingdom. Prehosp Disaster Med. 2017;32(6):667–672.

Keywords

ambulancesurveillancesyndromic surveillance
Type
Brief Reports
Information
Prehospital and Disaster Medicine , Volume 32 , Issue 6 , December 2017 , pp. 667 - 672
Copyright
© World Association for Disaster and Emergency Medicine 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Conflicts of interest: none

References

1. Triple, S Project. Assessment of syndromic surveillance in Europe. Lancet. 2011;378(9806):1833-1834.Google Scholar
2. Cooper, DL, Verlander, NQ, Elliot, AJ, et al. Can syndromic thresholds provide early warning of national influenza outbreaks? J Public Health. 2009;31(1):17-25.CrossRefGoogle ScholarPubMed
3. Edge, VL, Pollari, F, Ng, LK, et al. Syndromic surveillance of Norovirus using over-the-counter sales of medications related to gastrointestinal illness. Can J Infec Dis Med Microbiol. 2006;17(4):235-241.Google Scholar
4. Smith, S, Smith, G, Olowokure, B, et al. Early spread of the 2009 influenza A (H1N1) pandemic in the United Kingdom--use of local syndromic data, May-August 2009. Euro Surveill. 2010;16(3):221-228.Google Scholar
5. Harcourt, S, Fletcher, J, Loveridge, P, et al. Developing a new syndromic surveillance system for the London 2012 Olympic and Paralympic Games. Epidemiol Infect. 2012;140(12):2152-2156.CrossRefGoogle ScholarPubMed
6. Elliot, AJ, Hughes, HE, Hughes, TC, et al. Establishing an emergency department syndromic surveillance system to support the London 2012 Olympic and Paralympic Games. Emerg Med J. 2012;29(12):954-960.CrossRefGoogle ScholarPubMed
7. Harcourt, S, Morbey, R, Loveridge, P, et al. Developing and validating a new national remote health advice syndromic surveillance system in England. J Public Health. 2017;39(1):184-192.Google Scholar
8. Greenko, J, Mostashari, F, Fine, A, et al. Clinical evaluation of the Emergency Medical Services (EMS) ambulance dispatch-based syndromic surveillance system, New York City. J Urban Health. 2003;80(1):i50-i56.Google Scholar
9. Harder, K, Andersen, P, Bæhr, I, et al. Electronic real-time surveillance for influenza-like illness: experience from the 2009 influenza A (H1N1) pandemic in Denmark. Euro Surveill. 2011;16(3):pii=19767.CrossRefGoogle ScholarPubMed
10. Coory, M, Kelly, H, Tippett, V. Assessment of ambulance dispatch data for surveillance of influenza-like illness in Melbourne, Australia. Public Health. 2009;123(2):163-168.Google Scholar
11. Thornes, JE, Fisher, PA, Rayment-Bishop, T, et al. Ambulance call-outs and response times in Birmingham and the impact of extreme weather and climate change. Emerg Med J. 2014;31(3):220-228.Google Scholar
12. West Midlands Ambulance Service NHS Foundation Trust. Web site. http://www.wmas.nhs.uk/about-wmas. Accessed May 2017.Google Scholar
13. Morbey, RA, Elliot, AJ, Charlett, A, et al. The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England. Bioinformatics. 2015;31(22):3660-3665.Google Scholar
14. Klaucke, DN, Buehler, JW, Thacker, SB, et al. Guidelines for evaluating surveillance systems. MMWR Morb Mortal Wkly Rep. 1988;37(Suppl 5):1-18.Google Scholar
15. Sears, MR, Johnston, NW. Understanding the September asthma epidemic. J Allergy Clin Immunol. 2007;120(3):526-529.Google Scholar
16. Mostashari, F, Fine, A, Das, D, et al. Use of ambulance dispatch data as an early warning system for communitywide influenza-like illness, New York City. J Urban Health. 2003;80(1):i43-i49.Google Scholar