A Fully Integrated Real-Time Detection, Diagnosis, and Control of Community Diarrheal Disease Clusters and Outbreaks (the INTEGRATE Project): Protocol for an Enhanced Surveillance System (Preprint)

McIntyre, Kirsty Marie ORCID: 0000-0003-1360-122X, Bolton, Frederick J ORCID: 0000-0002-9685-9738, Christley, Rob M ORCID: 0000-0001-9250-3032, Cleary, Paul ORCID: 0000-0001-9335-5422, Deja, Elizabeth ORCID: 0000-0002-3626-4927, Durie, Ann E ORCID: 0000-0002-4121-8247, Diggle, Peter J ORCID: 0000-0003-3521-5020, Hughes, Dyfrig A ORCID: 0000-0001-8247-7459, de Lusignan, Simon ORCID: 0000-0002-8553-2641, Orton, Lois ORCID: 0000-0002-9641-523X
et al (show 8 more authors) (2019) A Fully Integrated Real-Time Detection, Diagnosis, and Control of Community Diarrheal Disease Clusters and Outbreaks (the INTEGRATE Project): Protocol for an Enhanced Surveillance System (Preprint).

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<sec> <title>BACKGROUND</title> <p>Diarrheal disease, which affects 1 in 4 people in the United Kingdom annually, is the most common cause of outbreaks in community and health care settings. Traditional surveillance methods tend to detect point-source outbreaks of diarrhea and vomiting; they are less effective at identifying low-level and intermittent food supply contamination. Furthermore, it can take up to 9 weeks for infections to be confirmed, reducing slow-burn outbreak recognition, potentially impacting hundreds or thousands of people over wide geographical areas. There is a need to address fundamental problems in traditional diarrheal disease surveillance because of underreporting and subsequent unconfirmed infection by patients and general practitioners (GPs); varying submission practices and selective testing of samples in laboratories; limitations in traditional microbiological diagnostics, meaning that the timeliness of sample testing and etiology of most cases remains unknown; and poorly integrated human and animal surveillance systems, meaning that identification of zoonoses is delayed or missed.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This study aims to detect anomalous patterns in the incidence of gastrointestinal disease in the (human) community; to target sampling; to test traditional diagnostic methods against rapid, modern, and sensitive molecular and genomic microbiology methods that identify and characterize responsible pathogens rapidly and more completely; and to determine the cost-effectiveness of rapid, modern, sensitive molecular and genomic microbiology methods.</p> </sec> <sec> <title>METHODS</title> <p>Syndromic surveillance will be used to aid identification of anomalous patterns in microbiological events based on temporal associations, demographic similarities among patients and animals, and changes in trends in acute gastroenteritis cases using a point process statistical model. Stool samples will be obtained from patients’ consulting GPs, to improve the timeliness of cluster detection and characterize the pathogens responsible, allowing health protection professionals to investigate and control outbreaks quickly, limiting their size and impact. The cost-effectiveness of the proposed system will be examined using formal cost-utility analysis to inform decisions on national implementation.</p> </sec> <sec> <title>RESULTS</title> <p>The project commenced on April 1, 2013. Favorable approval was obtained from the Research Ethics Committee on June 15, 2015, and the first patient was recruited on October 13, 2015, with 1407 patients recruited and samples processed using traditional laboratory techniques as of March 2017.</p> </sec> <sec> <title>CONCLUSIONS</title> <p>The overall aim of this study is to create a new One Health paradigm for detecting and investigating diarrhea and vomiting in the community in near-real time, shifting from passive human surveillance and management of laboratory-confirmed infection toward an integrated, interdisciplinary enhanced surveillance system including management of people with symptoms.</p> </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> <p>DERR1-10.2196/13941</p> </sec>

Item Type: Article
Depositing User: Symplectic Admin
Date Deposited: 02 Dec 2019 13:39
Last Modified: 19 Jan 2023 00:17
DOI: 10.2196/preprints.13941
Open Access URL: https://www.researchprotocols.org/2019/9/e13941/
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3064440