ISDS Conference

This paper assesses the possible interest of using of a syndromic surveillance system based on emergency departments (ED) and crude mortality for monitoring health effects of climate changes (CC).

This abstract describes an Electronic Surveillance System for Infectious Disease Outbreaks used by all federal levels in Germany and comments on timelyness and comprehensiveness of informations about outbreak settings and infection sources.

As part of the epidemiological investigation of an outbreak of Q fever in a factory in Scotland, we aimed to utilise a spatial scan statistic to aid in identification of areas associated with increased relative risk of infection.

This paper describes several interactive data exploration tools for examining surveillance data.

To determine sensitivity and specificity of syndromic surveillance of influenza based on data from SOS Medecins, a healthcare network of emergency general practitioners (GP) in Bordeaux, France.

This paper describes a method to predict syndromic data for surveillance of public health using the method of recursive least squares and a new method of correcting for the day of week effect in order to have a prediction of the background upon which detections of actual events can be computed

A common problem in syndromic surveillance using ED department data is temporary gaps in the data received from individual ED departments caused by delays in receiving the data.

Currently most syndromic surveillance systems provide information about the status of the data sources feeding into the system, for example on the home page of the system, but do not show the effects of any missing data sources on individual derived data elements (except in that graphs may show obvious drops in counts on days when data sources are missing).

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.

Influenza affects millions of people and causes about 36,000 deaths in the United States each winter. Pandemics of influenza emerge at irregular intervals. National influenza surveillance is used to detect the emergence and spread of influenza virus variants and to monitor influenza-related morbidity and mortality. Existing surveillance consists of seven data types, which are reported weekly. Newly available national electronic data sources created as part of the routine delivery of medical care might supplement current data sources. Nurse call data offer national coverage, are timely, and do not require any extra manual data entry. Using such data for influenza-like illness (ILI) surveillance may lead to earlier detection of ILI in the community, both because people with ILI may call a nurse line before seeking care at a health-care facility and because the data are more timely than existing weekly data.

Objective

Our purpose was to compare nurse call data for respiratory and ILI against CDC national influenza surveillance data from the 2004-2005 season by region to determine if the call data were informative and might allow earlier detection of influenza activity.

Calls to NHS Direct (a national UK telephone health advice line) which may be indicative of infection show marked seasonal variation, often peaking during winter or early spring. This variation may be related to the seasonality of common viruses. There is currently no routine microbiological confirmation of the cause of illness in NHS Direct callers. Modelling trends in NHS Direct syndromic call data against laboratory data may help by attributing the likely cause of these calls the and surveillance ‘signals’ generated by syndromic surveillance.

Multiple linear regression has been used previously to estimate the contribution of rotavirus and RSV to hospital admission for infectious intestinal disease and lower respiratory tract infections respectively. We applied a similar regression model to NHS Direct syndromic surveillance data and laboratory reports.

Objective

To provide weekly estimates of the proportions of NHS Direct respiratory calls attributable to common infectious disease pathogens.