Literature DB >> 20951829

Outpatient physician billing data for age and setting specific syndromic surveillance of influenza-like illnesses.

Emily H Chan1, Robyn Tamblyn, Katia M L Charland, David L Buckeridge.   

Abstract

Syndromic surveillance is a novel automated approach to monitoring influenza activity, but there is no consensus regarding the most informative data sources for use within such a system. By comparing physician billing data from Quebec, Canada and hospital admission records, we assessed the timeliness of medical visits for influenza-like illnesses (ILI) to two types of outpatient healthcare settings. Overall, ILI visits by children aged 5-17 years at community-based settings were the most strongly correlated with hospital admissions and gave the greatest lead over hospital admissions. However, a degree of year-to-year variation suggests that syndromic surveillance of influenza should not focus on just a single subgroup. These findings reveal the richness of these real-time data for epidemic monitoring and demonstrate the flexibility of syndromic surveillance. By using real-time data, an evolving epidemic can be rapidly characterized by its epidemiological patterns, which is not possible with traditional surveillance systems.
Copyright © 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20951829     DOI: 10.1016/j.jbi.2010.10.001

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  9 in total

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Review 3.  Influenza surveillance systems using traditional and alternative sources of data: A scoping review.

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Journal:  Influenza Other Respir Viruses       Date:  2022-09-08       Impact factor: 5.606

4.  Influenza-related healthcare visits, hospital admissions, and direct medical costs for all children aged 2 to 17 years in a defined Swedish region, monitored for 7 years.

Authors:  Mikael Rahmqvist; Kristian Gjessing; Tomas Faresjö
Journal:  Medicine (Baltimore)       Date:  2016-08       Impact factor: 1.889

5.  Sustaining Antimicrobial Stewardship in a High-Antibiotic Resistance Setting.

Authors:  Tat Ming Ng; Shi Thong Heng; Boon Hou Chua; Li Wei Ang; Sock Hoon Tan; Hui Lin Tay; Min Yi Yap; Jason Quek; Christine B Teng; Barnaby E Young; Ray Lin; Brenda Ang; Tau Hong Lee; David C Lye
Journal:  JAMA Netw Open       Date:  2022-05-02

6.  Predictive validation of an influenza spread model.

Authors:  Ayaz Hyder; David L Buckeridge; Brian Leung
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7.  The accuracy and timeliness of neuraminidase inhibitor dispensing data for predicting laboratory-confirmed influenza.

Authors:  J Papenburg; K M Charland; G DE Serres; D L Buckeridge
Journal:  Epidemiol Infect       Date:  2015-11-27       Impact factor: 4.434

8.  Comprehensive real-time epidemiological data from respiratory infections in Finland between 2010 and 2014 obtained from an automated and multianalyte mariPOC® respiratory pathogen test.

Authors:  M Gunell; P Antikainen; N Porjo; K Irjala; J Vakkila; K Hotakainen; S S Kaukoranta; J J Hirvonen; K Saha; R Manninen; B Forsblom; K Rantakokko-Jalava; V Peltola; J O Koskinen; P Huovinen
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2016-01-06       Impact factor: 3.267

9.  Can syndromic surveillance help forecast winter hospital bed pressures in England?

Authors:  Roger A Morbey; Andre Charlett; Iain Lake; James Mapstone; Richard Pebody; James Sedgwick; Gillian E Smith; Alex J Elliot
Journal:  PLoS One       Date:  2020-02-10       Impact factor: 3.240

  9 in total

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