Elliot Alex

Within the UK, previous syndromic surveillance studies have used statistical estimation to describe the activity of respiratory pathogens. The Emergency Department Syndromic Surveillance System (EDSSS) was initially developed in preparation of the London 2012 Olympic and Paralympic Games and has continued as a standard surveillance system, with expanding coverage across England and Northern Ireland. All reporting to this system is completely passive, with no extra work required within the ED. The data collection includes the diagnosis for each attendance, where available, using the coding system in use locally. The coding varies by ED with ICD- 10, Snomed-CT and the less detailed NHS Accident and Emergency Diagnosis Tables all in use. The use of diagnosis coding systems with differing levels of detail creates the need to have a variety of syndromic indicators to make best use of the data received.

We aim to describe the trends in respiratory attendances, and their comparison to the known circulating pathogens identified though laboratory surveillance to establish if any single syndromic indicator may be attributed to any one pathogen in particular. We also aim to describe the flexibility in the development of EDSSS syndromic indicators to best fit the data received.

Objective

Can syndromic surveillance using standard emergency department data collected using automated daily extraction be used to describe and alert the onset of the seasonal activity of named respiratory pathogens within the community?

We assessed the impact of the London 2012 Olympic and Paralympic Games on syndromic surveillance systems including the incidence of syndromic indictors and total contacts with health care.

Introduction

Mass gatherings can impact on the health of the public including importation of infectious diseases, exposure of international visitors to endemic diseases in the host country and the increased risk of bioterrorist activity. Public health surveillance during mass gatherings therefore affords an opportunity to identify, and quantify any impact (or reassure on the absence of impact) on public health in a timely manner. In preparation for the Games, Public Health England undertook a programme of work to expand the existing suite of syndromic surveillance systems to include daily general practitioner out of hours (GPOOH) consultations and emergency department (ED) attendances at sentinel sites. These new systems complemented existing syndromic surveillance systems offering the opportunity to monitor trends in patient contacts with GPs outside of normal day time opening hours, as well as potentially the more severe end of the disease spectrum which would present at EDs. We assessed the impact of the 2012 Olympics on national surveillance systems, comparing to periods before and after the Games and in previous years and also the impact of specific events during the Games.

Public Health England has developed a suite of syndromic surveillance systems, collecting data from a number of health care sources, and linking to public health action to try and improve the public health benefit of the surveillance.1 We aim to describe this national syndromic service, highlighting the flexibility of the systems in responding to a range of environmental incidents.

Objective

To deliver a national syndromic surveillance service, linking analytical and statistical methods with public health action to provide surveillance support for national public health programmes monitoring the spread of infectious diseases and the health impact of environmental incidents in England.

Whilst the sensitivity and specificity of traditional laboratory-based surveillance can be readily estimated, the situation is less clear cut for syndromic surveillance. Syndromic surveillance indicators based upon presenting symptoms, chief complaints or preliminary diagnoses are designed to provide public health systems with support to detect multiple potential threats to public health. There is however, no gold standard list of all the possible ‘events’ that should have been detected. This is especially true in emergency response where systems are designed to detect possible events for which there is no directly comparable historical precedent.

Objective

To devise a methodology for validating the effectiveness of syndromic surveillance systems across a range of public health scenarios, even in the absence of historical example datasets.

Seasonal rises in respiratory illnesses are a major burden on primary care services. Public Health England (PHE), in collaboration with NHS 111, coordinate a national surveillance system based upon the daily calls received at the NHS 111 telehealth service. Daily calls are categorized according to the clinical ‘pathway’ used by the call handler to assess the presenting complaints of the caller e.g. cold/flu, diarrhoea, rash.

Objective

We compared weekly laboratory reports for a number of seasonal respiratory pathogens with telehealth calls (NHS 111) to assess the burden of seasonal pathogens on this syndromic surveillance system and investigate any potential for providing additional early warning of seasonal outbreaks.

Syndromic surveillance systems often produce large numbers of detections due to excess activity (alarms) in their indicators. Few alarms are classified as alerts (public health events that may require a response). Decision-making in syndromic surveillance as to whether an alarm requires a response (alert) is often entirely based on expert knowledge. These approaches (known as heuristics) may work well and produce faster results than automated processes (known as normative), but usually rely on the expertise of a small group of experts who hold much of their knowledge implicitly. The effectiveness of syndromic surveillance systems could be compromised in the absence of experts, which may significantly impact their response during a public health emergency. Also, there may be patterns and relations in the data not recognised by the experts. Structural learning provides a mechanism to identify relations between syndromic indicators and the relations between these indicators and alerts. Their outputs could be used to help decision makers determine more effectively which alarms are most likely to lead to alerts. A normative approach may reduce the reliance of the decision making process on key individuals

Objective

To analyse the use of Bayesian network structural learning to identify relations between syndromic indicators which could inform decision-making processes

While results from syndromic surveillance systems are commonly presented in the literature, few systems appear to have been thoroughly evaluated to examine which events can and cannot be detected, the time to detection and the efficacy of different syndromic surveillance data streams. Such an evaluation framework is presented.

Objective

To devise a methodology for evaluating the effectiveness of syndromic surveillance systems

Real-time syndromic surveillance requires daily surveillance of a range of health data sources. Most real-time data sources from health care systems exhibit large day of the week fluctuations as service provision and patient behaviour varies by day of the week. Regular day of the week effects are further complicated by the occurrence of public holidays (usually 8 per year in England), which can limit the availability of certain services and affect patient behaviour. Simple seven day moving averages fail to provide a smoothed trend around public holidays and can lead to false alarms or potentially delays in detection of outbreaks.

Objective

To develop smoothing techniques for daily syndromic surveillance data that allow for the easier identification of trends and unusual activity independent of day of the week and holiday effects.

When monitoring public health incidents using syndromic surveillance systems, Public Health England (PHE) uses the age of the presenting patient as a key indicator to further assess the severity, impact of the incident, and to provide intelligence on the likely cause. However the age distribution of cases is usually not considered until after unusual activity has been identified in the allages population data. We assessed whether monitoring specific age groups contemporaneously could improve the timeliness, specificity and sensitivity of public health surveillance.

Objective

To investigate whether aberration detection methods for syndromic surveillance would be more useful if data were stratified by age band.