Aspen Hammond1, John J Kim1,2, Holly Sadler1, Katelijn Vandemaele1. 1. Global Influenza Programme, World Health Organization, Geneva, Switzerland. 2. School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada.
Abstract
OBJECTIVE: While the World Health Organization's recommendation of syndromic sentinel surveillance for influenza is an efficient method to collect high-quality data, limitations exist. Aligned with the Research Recommendation 1.1.2 of the WHO Public Health Research Agenda for Influenza-to identify reliable complementary influenza surveillance systems which provide real-time estimates of influenza activity-we performed a scoping review to map the extent and nature of published literature on the use of non-traditional sources of syndromic surveillance data for influenza. METHODS: We searched three electronic databases (PubMed, Web of Science, and Scopus) for articles in English, French, and Spanish, published between January 1 2007 and January 28 2022. Studies were included if they directly compared at least one non-traditional with a traditional influenza surveillance system in terms of correlation in activity or timeliness. FINDINGS: We retrieved 823 articles of which 57 were included for analysis. Fifteen articles considered electronic health records (EHR), 11 participatory surveillance, 10 online searches and webpage traffic, seven Twitter, five absenteeism, four telephone health lines, three medication sales, two media reporting, and five looked at other miscellaneous sources of data. Several articles considered more than one non-traditional surveillance method. CONCLUSION: We identified eight categories and a miscellaneous group of non-traditional influenza surveillance systems with varying levels of evidence on timeliness and correlation to traditional surveillance systems. Analyses of EHR and participatory surveillance systems appeared to have the most agreement on timeliness and correlation to traditional systems. Studies suggested non-traditional surveillance systems as complements rather than replacements to traditional systems.
OBJECTIVE: While the World Health Organization's recommendation of syndromic sentinel surveillance for influenza is an efficient method to collect high-quality data, limitations exist. Aligned with the Research Recommendation 1.1.2 of the WHO Public Health Research Agenda for Influenza-to identify reliable complementary influenza surveillance systems which provide real-time estimates of influenza activity-we performed a scoping review to map the extent and nature of published literature on the use of non-traditional sources of syndromic surveillance data for influenza. METHODS: We searched three electronic databases (PubMed, Web of Science, and Scopus) for articles in English, French, and Spanish, published between January 1 2007 and January 28 2022. Studies were included if they directly compared at least one non-traditional with a traditional influenza surveillance system in terms of correlation in activity or timeliness. FINDINGS: We retrieved 823 articles of which 57 were included for analysis. Fifteen articles considered electronic health records (EHR), 11 participatory surveillance, 10 online searches and webpage traffic, seven Twitter, five absenteeism, four telephone health lines, three medication sales, two media reporting, and five looked at other miscellaneous sources of data. Several articles considered more than one non-traditional surveillance method. CONCLUSION: We identified eight categories and a miscellaneous group of non-traditional influenza surveillance systems with varying levels of evidence on timeliness and correlation to traditional surveillance systems. Analyses of EHR and participatory surveillance systems appeared to have the most agreement on timeliness and correlation to traditional systems. Studies suggested non-traditional surveillance systems as complements rather than replacements to traditional systems.
Authors: Anna C Nagel; Ming-Hsiang Tsou; Brian H Spitzberg; Li An; J Mark Gawron; Dipak K Gupta; Jiue-An Yang; Su Han; K Michael Peddecord; Suzanne Lindsay; Mark H Sawyer Journal: J Med Internet Res Date: 2013-10-24 Impact factor: 5.428
Authors: Florian Rohart; Gabriel J Milinovich; Simon M R Avril; Kim-Anh Lê Cao; Shilu Tong; Wenbiao Hu Journal: Sci Rep Date: 2016-12-20 Impact factor: 4.379
Authors: Carina Aguilar Martín; Mª Rosa Dalmau Llorca; Elisabet Castro Blanco; Noèlia Carrasco-Querol; Zojaina Hernández Rojas; Emma Forcadell Drago; Dolores Rodríguez Cumplido; Alessandra Queiroga Gonçalves; José Fernández-Sáez Journal: Int J Environ Res Public Health Date: 2022-01-23 Impact factor: 3.390
Authors: Nicole Rosenkötter; Alexandra Ziemann; Luis Garcia-Castrillo Riesgo; Jean Bernard Gillet; Gernot Vergeiner; Thomas Krafft; Helmut Brand Journal: BMC Public Health Date: 2013-10-01 Impact factor: 3.295