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Morbey Roger

Description

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

Submitted by teresa.hamby@d… on
Description

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

Submitted by teresa.hamby@d… on
Description

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.

Submitted by teresa.hamby@d… on
Description

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.

 

Submitted by Magou on
Description

Syndromic surveillance systems are used by Public Health England (PHE) to detect changes in health care activity that are indicative of potential threats to public health. By providing early warning and situational awareness, these systems play a key role in supporting infectious disease surveillance programmes, decision making and supporting public health interventions. In order to improve the identification of unusual activity, we created new baselines to model seasonally expected activity in the absence of outbreaks or other incidents. Although historical data could be used to model seasonality, changes due to public health interventions or working practices affected comparability. Specific examples of these changes included a major change in the way telehealth services were provided in England and the rotavirus vaccination programme introduced in July 2013 that changed the seasonality of gastrointestinal consultations. Therefore, we needed to incorporate these temporal changes in our baselines.

Objective

To improve the ability of syndromic surveillance systems to detect unusual events.

Submitted by Magou on
Description

Public Health England (PHE) uses syndromic surveillance systems to monitor for seasonal increases in respiratory illness. Respiratory illnesses create a considerable burden on health care services and therefore identifying the timing and intensity of peaks of activity is important for public health decision-making. Furthermore, identifying the incidence of specific respiratory pathogens circulating in the community is essential for targeting public health interventions e.g. vaccination. Syndromic surveillance can provide early warning of increases, but cannot explicitly identify the pathogens responsible for such increases.

PHE uses a range of general and specific respiratory syndromic indicators in their syndromic surveillance systems, e.g. “all respiratory disease”, “influenza-like illness”, “bronchitis” and “cough”. Previous research has shown that “influenza-like illness” is associated with influenza circulating in the community1 whilst “cough” and “bronchitis” syndromic indicators in children under 5 are associated with respiratory syncytial virus (RSV)2, 3. However, the relative burden of other pathogens, e.g. rhinovirus and parainfluenza is less well understood. We have sought to further understand the relationship between specific pathogens and syndromic indicators and to improve estimates of disease burden. Therefore, we modelled the association between pathogen incidence, using laboratory reports and health care presentations, using syndromic data. 

Objective

To improve understanding of the relative burden of different causative respiratory pathogens on respiratory syndromic indicators monitored using syndromic surveillance systems in England. 

Submitted by Magou on
Description

From 1 September 2015, babies in the United Kingdom (UK) born on/after 1 July 2015 became eligible to receive the MenB vaccine, given at 2 and 4 months of age, with a booster at 12 months. Early trials found a high prevalence of fever (over 38°C) in babies given the vaccine with other routine vaccines at 2 and 4 months. We used syndromic surveillance data to assess whether there had been increased family doctor (general practitioner (GP)) consultations for fever in young infants following the introduction of the vaccine. 

Objective

To use syndromic surveillance data to assess whether there has been an increase in GP fever consultations since the inclusion of the meningococcal B (MenB) vaccine in the UK vaccination schedule. 

Submitted by Magou on

Public Health England uses data from four national syndromic surveillance systems to support public health programmes and identify unusual activity. Each system monitors a wide range of respiratory, gastrointestinal and other syndromes at a local, regional and national level. As a result, over 12,000 ‘signals’ (combining syndrome and geography) need to be assessed each day to identify aberrations. In this webinar I will describe how the ‘big data’ collected daily are translated into useful information for public health surveillance.