Elliot Alex

Co-financed by the European Commission through the Executive Agency for Health and Consumers, the European Triple-S project (Syndromic Surveillance Survey, Assessment towards Guidelines for Europe) was launched in 2010 for a 3-year period [1]. It involves 24 organisations from 13 countries. The project's final purpose is to increase the European capacity for real-time surveillance and monitoring of the health burden of expected and unexpected health-related events. Based on inventories of European SyS systems [2;3], eight country visits [4] and experts advice, the project has developed scientific guidelines that aim at providing scientific and technical guidance for the development and implementation of SyS systems for both human and animal health.

Objective

To present the Guidelines for implementing syndromic surveillance (SyS) systems at a national, regional or local level.

Routine primary care data provide the means to systematically monitor a variety of syndromes which could give early warning of health protection issues (microbiological and chemical). It is possible to track milder illnesses which may not present to hospitals (e.g. chicken pox, conjunctivitis) or illnesses for which laboratory specimens are not routinely taken (e.g. influenza). Real-time data are also needed to respond to major health protection incidents.

Objective

To describe the arrangements for Primary Care Surveillance in the UK and provide examples of the benefits of this work for Public Health.

Air pollution is well documented to cause adverse health effects in the population. Epidemiological/toxicological studies have demonstrated that air pollution is associated with various adverse health outcomes, ranging from mortality to subclinical respiratory symptoms. Classical epidemiological studies of the health effects of air pollution are typically retrospective. In order to assess the effectiveness of any public health messages or interventions in a timely manner there is a need to be able to systematically detect any health effects occurring in real-time. The UK syndromic surveillance systems are coordinated by Public Health England (PHE) and are used to monitor infectious diseases in real-time. This study is the first in the UK to explore whether syndromic surveillance systems can detect public health impacts associated with air pollution events.

Objective: This study examined whether the current UK real-time syndromic surveillance systems can detect public health impacts associated with air pollution events such as fires and ambient air pollution episodes.

Wetter and stormier weather is predicted in the UK as global temperatures rise. It is likely there will be increases in river and coastal flooding. The known short and medium term health effects of flooding are drowning, injury, acute asthma, skin rashes and outbreaks of gastrointestinal and respiratory disease. Longer term health effects of flooding are thought to be psychological stress and increased rates of mental illness. Reacher et al. conducted a retrospective study of illness in a population affected by flooding in Lewes, South-East England during 2000 [1]. They found a significant raised risk of earache (RR=2.2) and gastroenteritis (RR=1.7) for flooded households. More striking was the higher level of psychological distress experienced by these residents (RR=4.1), which may have also explained some of the excess physical illness.

Objective

This paper describes the results of prospective real time syndromic surveillance conducted during a national flooding incident during 2007 in the UK.

Globally, there have been various studies assessing trends in Google search terms in the context of public health surveillance1. However, there has been a predominant focus on individual health outcomes such as influenza, with limited evidence on the added value and practical impact on public health action for a range of diseases and conditions routinely monitored by existing surveillance programmes. A proposed advantage is improved timeliness relative to established surveillance systems. However, these studies did not compare performance against other syndromic data sources, which are often monitored daily and already offer early warning over traditional surveillance methods. Google search data could also potentially contribute to assessing the wider population health impact of public health events by supporting estimation of the proportion of the population who are symptomatic but may not present to healthcare services.

Objective:

To carry out an observational study to explore what added value Google search data can provide to existing routine syndromic surveillance systems in England for a range of conditions of public health importance and summarise lessons learned for other countries.

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.

Public Health England's syndromic surveillance service monitor presentations for gastrointestinal illness to detect increases in health care seeking behaviour driven by infectious gastrointestinal disease. We use regression models to create baselines for expected activity and then identify any periods of signficant increases. The introduction of a rotavirus vaccine in England during July 2013 (Bawa, Z. et al. 2015) led to a reduction in incidence of the disease, requiring a readjustment of baselines.

Objective:

To adjust modelled baselines used for syndromic surveillance to account for public health interventions. Specifically to account for a change in the seasonality of diarrhoea and vomiting indicators following the introduction of a rotavirus vaccine in England.

Syndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.

Objective:

To investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.