Chapter 4.9 Real-time syndromic surveillance
Authors: Elliot AJ, Hughes HE, Harcourt SE, Morbey RA, Smith S, Smith GE.
Chapter 4.9 describes the key factors underpinning real-time syndromic surveillance systems and the use of syndromic surveillance data in research, including:
- The definition of syndromic surveillance.
- Data sources for syndromic surveillance.
- Governance issues.
- Data analysis and statistics.
- The application of syndromic surveillance in research.
What is this chapter about?
Innovative methods are needed to ensure timely and appropriate public health surveillance in Health EDRM. Syndromic surveillance is the near real-time collection, analysis, interpretation and dissemination of health-related data to help decision-makers with the early identification of the impact (or absence of impact) of potential health threats.
This chapter provides an introduction to syndromic surveillance and outlines several advantages of its methods over other ways of data gathering. These advantages include early warning, situational analysis, reassurance and flexibility. The chapter provides insight on data sources for syndromic surveillance, governance issues, analysis of syndromic surveillance data and the practical application of syndromic surveillance in Health EDRM research.
Case studies presented in the chapter:
- Assessing potential health impacts of mass gatherings and sporting events using syndromic surveillance.
- Assessing the impact of air pollution on health using syndromic surveillance.
- Determining the likely impact of a new vaccine programme using syndromic surveillance.
What are the key messages of this chapter?
- Syndromic surveillance systems can augment existing public health surveillance programmes, providing early warning and introducing real-time intelligence and reassurance at a national, regional and local level.
- Compared to traditional surveillance systems, syndromic surveillance can provide a more flexible approach to surveillance, enabling multi-purpose surveillance including emerging threats.
- Adherence to good governance and data security practices around the collection, storage, processing and use of syndromic surveillance data is essential for the long-term success of systems.
- Syndromic surveillance data are a valuable resource for public health research, including in Health EDRM, but specific limitations of syndromic surveillance for research need to be considered.
- Syndromic surveillance systems gain value in research data sources when operated consistently over time enabling comparison to historical data.
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