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Lake Iain

Description

The negative effect of air pollution on human health is well documented illustrating increased risk of respiratory, cardiac and other health conditions. Currently, during air pollution episodes Public Health England (PHE) syndromic surveillance systems provide a near real-time analysis of the health impact of poor air quality. In England, syndromic surveillance has previously been used on an ad hoc basis to monitor health impact; this has usually happened during widespread national air pollution episodes where the air pollution index has reached "High"™ or "Very High"™ levels on the UK Daily Air Quality Index (DAQI). We now aim to undertake a more systematic approach to understanding the utility of syndromic surveillance for monitoring the health impact of air pollution. This would improve our understanding of the sensitivity and specificity of syndromic surveillance systems for contributing to the public health response to acute air pollution incidents; form a baseline for future interventions; assess whether syndromic surveillance systems provide a useful tool for public health alerting; enable us to explore which pollutants drive changes in health-care seeking behaviour; and add to the knowledge base.

Objective:

To explore the utility of syndromic surveillance systems for detecting and monitoring the impact of air pollution incidents on health-care seeking behaviour in England between 2012 and 2017.

Submitted by elamb on
Description

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.

Submitted by Magou on
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