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Spector E, Zhang Y, Guo Y, Bost S, Yang X, Prosperi M, Wu Y, Shao H, Bian J. Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19084673. [PMID: 35457541 PMCID: PMC9026395 DOI: 10.3390/ijerph19084673] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/02/2022] [Accepted: 04/06/2022] [Indexed: 11/16/2022]
Abstract
Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.
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Affiliation(s)
- Eliot Spector
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, USA; (E.S.); (Y.G.); (S.B.); (X.Y.); (Y.W.)
| | - Yahan Zhang
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL 32610, USA; (Y.Z.); (H.S.)
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, USA; (E.S.); (Y.G.); (S.B.); (X.Y.); (Y.W.)
| | - Sarah Bost
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, USA; (E.S.); (Y.G.); (S.B.); (X.Y.); (Y.W.)
| | - Xi Yang
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, USA; (E.S.); (Y.G.); (S.B.); (X.Y.); (Y.W.)
| | - Mattia Prosperi
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, USA;
| | - Yonghui Wu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, USA; (E.S.); (Y.G.); (S.B.); (X.Y.); (Y.W.)
| | - Hui Shao
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL 32610, USA; (Y.Z.); (H.S.)
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, USA; (E.S.); (Y.G.); (S.B.); (X.Y.); (Y.W.)
- Correspondence:
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