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Using large-scale syndromic datasets to support epidemiology and surveillance
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Todkill, Daniel (2021) Using large-scale syndromic datasets to support epidemiology and surveillance. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b3927868
Abstract
Using large-scale syndromic datasets to support epidemiology and surveillance
Healthcare and the healthcare industry have traditionally produced huge amounts of data and information; patient care necessitates accurate record keeping, records of attendances and often details of the reason for contact with healthcare and outcomes.7 During the past decade, there has been a dramatic shift to digitize healthcare related information, with a view to both increasing efficiencies in these areas, and to generate new insights.8 These rich, but often unstructured data sources can present both opportunities and challenges to data scientists and epidemiologists. Syndromic surveillance (SS) is the real-time (or near real-time) collection, analysis, interpretation, and dissemination of health-related data to enable the early identification of the impact (or absence of impact) of potential human or veterinary public-health threats which require effective public-health action.9 In England, Public Health England (PHE) coordinates a suite of national real-time syndromic surveillance systems. Underpinning their operation is the collation, analysis and interpretation of large-scale datasets (“big data”).
This PhD by Published Works describes work which has evaluated, developed or utilised a number of these large healthcare datasets for both surveillance and epidemiology of public health events. The thesis is divided into four themes covering critical aspects of SS. Firstly, developing SS systems using novel data sources; something which is currently under-reported in the literature. Secondly, using syndromic data systems for non-infectious disease epidemiology; understanding how these systems can inform public health insight and action outside of their original remit. Thirdly, determining the utility in identifying outbreaks which was one of the original envisioned purposes of SS, using gastrointestinal illness (GI) as a case-study. The final theme is understanding how SS is used in the context of mass gatherings; again, a key original aspect of syndromic surveillance.
The thesis collates a portfolio of indexed works, all of which use (combined with other data sources) large, health-related data collated and operated by the PHE Real-Time Syndromic Surveillance Team (ReSST) and employ a range of different methodologies to translate data into public health action. These include describing the development of a novel system, observational studies and time series analysis.
Key findings from the papers include; learning how to develop these systems, demonstration of their utility in non-infectious disease epidemiology, leading to new insights into the socio-demographic distribution and causes of presentations to healthcare with Allergic Rhinitis, understanding the challenges and limitations of syndromic surveillance in identifying outbreaks of GI disease and how they can be used during mass gatherings.
Using diverse methodologies and data as a collective, the papers have led to significant public health impacts; both in terms of how these systems are used in England currently and how they have influenced global development of this small but growing speciality
Item Type: | Thesis (PhD) | ||||
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Subjects: | R Medicine > R Medicine (General) R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
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Library of Congress Subject Headings (LCSH): | Public health surveillance, Medical records -- Data processing, Medicine -- Data processing, Epidemiology -- Data processing, Data sets, Medical informatics | ||||
Official Date: | November 2021 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Warwick Medical School | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Format of File: | |||||
Extent: | 58 pages : illustrations | ||||
Language: | eng |
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