Self-Reported Fever and Measured Temperature in Emergency Department Records Used for Syndromic Surveillance

Many public health agencies monitor population health using syndromic surveillance, generally employing information from emergency department (ED) visit records. When combined with other information, objective evidence of fever may enhance the accuracy with which surveillance systems detect syndromes of interest, such as influenza-like illness. This study found that patient chief complaint of self-reported fever was more readily available in ED records than measured temperature and that the majority of patients with an elevated temperature recorded also self-reported fever. Due to its currently limited availability, we conclude that measured temperature is likely to add little value to self-reported fever in syndromic surveillance for febrile illness using ED records

The use of emergency department electronic health data for syndromic surveillance to enhance public health surveillance programmes in England

Public health surveillance allows for the identification and monitoring of trends in human health. Syndromic surveillance is a relatively recent addition to these activities, offering the potential to monitor trends on a (near) real-time basis and is often more timely than may be possible through other, traditional, surveillance routes. Emergency department (ED) syndromic surveillance systems have been developed and successfully operated worldwide. The Public Health England Emergency Department Syndromic Surveillance System (EDSSS) was developed in preparation for the London 2012 Olympic and Paralympic Games and remains as a public health legacy of the Games. This thesis aimed to describe and provide evidence of how emergency department syndromic surveillance (as performed by EDSSS) provides additional benefit to public health surveillance and added value to emergency care services in England. Additionally the potential for further development and future improvements to public health surveillance is described. The EDSSS is shown here to have been successfully used to describe the impact of the rotavirus vaccine, indicating that EDSSS has the potential to be used for future rapid, stand alone, investigation of impact of vaccines in England. In the first cross-national study of its kind, the EDSSS (alongside OSCOUR, its counterpart in France) was successfully used to describe the changes in human health indicators during periods of poor air quality. In addition to reporting on both infectious and non-infectious disease, emergency department syndromic surveillance also successfully described the impacts of human behaviour on ED attendances. During the EURO 2016 football tournament ED attendances were found to differ from the expected during match periods, not only in France the host country, but also in the UK home nations where fans followed team progress from home. The EDSSS is also the first example of a syndromic surveillance system having input into the development of a standardised national dataset, which has been mandated across EDs in England. Primarily aimed to improve patient care and the wider workings of EDs, this improved data collection has resulted in improvements in the EDSSS itself, which was subsequently expanded from a small sentinel to truly national surveillance system. The standardisation of ED data collection and reporting, alongside improved geographical coverage and near real-time surveillance reporting, enabled rapid feedback on the impact of the COVID-19 pandemic on ED attendances in England. EDSSS described general trends in ED attendances, encompassing both infectious and non-infectious indicators, prompting the refinement of public health messaging, encouraging continued use of emergency care as required by the general public. The evidence presented in this thesis has demonstrated where the ED syndromic surveillance has added value for public health surveillance in England, utilising the system flexibility and timeliness of reporting. Successful collaborative working has provided the potential for future cross-system learning for further system development, as well as the ability to work at local, national and potentially international scales

Real-time classifiers from free-text for continuous surveillance of small animal disease

A wealth of information of epidemiological importance is held within unstructured narrative clinical records. Text mining provides computational techniques for extracting usable information from the language used to communicate between humans, including the spoken and written word. The aim of this work was to develop text-mining methodologies capable of rendering the large volume of information within veterinary clinical narratives accessible for research and surveillance purposes. The free-text records collated within the dataset of the Small Animal Veterinary Surveillance Network formed the development material and target of this work. The efficacy of pre-existent clinician-assigned coding applied to the dataset was evaluated and the nature of notation and vocabulary used in documenting consultations was explored and described. Consultation records were pre-processed to improve human and software readability, and software was developed to redact incidental identifiers present within the free-text. An automated system able to classify for the presence of clinical signs, utilising only information present within the free-text record, was developed with the aim that it would facilitate timely detection of spatio-temporal trends in clinical signs. Clinician-assigned main reason for visit coding provided a poor summary of the large quantity of information exchanged during a veterinary consultation and the nature of the coding and questionnaire triggering further obfuscated information. Delineation of the previously undocumented veterinary clinical sublanguage identified common themes and their manner of documentation, this was key to the development of programmatic methods. A rule-based classifier using logically-chosen dictionaries, sequential processing and data-masking redacted identifiers while maintaining research usability of records. Highly sensitive and specific free-text classification was achieved by applying classifiers for individual clinical signs within a context-sensitive scaffold, this permitted or prohibited matching dependent on the clinical context in which a clinical sign was documented. The mean sensitivity achieved within an unseen test dataset was 98.17 (74.47, 99.9)% and mean specificity 99.94 (77.1, 100.0)%. When used in combination to identify animals with any of a combination of gastrointestinal clinical signs, the sensitivity achieved was 99.44% (95% CI: 98.57, 99.78)% and specificity 99.74 (95% CI: 99.62, 99.83). This work illustrates the importance, utility and promise of free-text classification of clinical records and provides a framework within which this is possible whilst respecting the confidentiality of client and clinician