Literature DB >> 21370780

A multi-function public health surveillance system and the lessons learned in its development: the Alberta Real Time Syndromic Surveillance Net.

Shihe Fan1, Corinne Blair, Angela Brown, Stephan Gabos, Lance Honish, Trina Hughes, Joy Jaipaul, Marcia Johnson, Eric Lo, Anna Lubchenko, Laura Mashinter, David P Meurer, Vanessa Nardelli, Gerry Predy, Liz Shewchuk, Daniel Sosin, Bryan Wicentowich, James Talbot.   

Abstract

OBJECTIVE: We describe a centralized automated multi-function detection and reporting system for public health surveillance--the Alberta Real Time Syndromic Surveillance Net (ARTSSN). This improves upon traditional paper-based systems which are often fragmented, limited by incomplete data collection and inadequate analytical capacity, and incapable of providing timely information for public health action.
METHODS: ARTSSN concurrently analyzes multiple electronic data sources in real time to describe results in tables, charts and maps. Detected anomalies are immediately disseminated via alerts to decision-makers for action.
RESULTS: ARTSSN provides richly integrated information on a variety of health conditions for early detection of and prompt action on abnormal events such as clusters, outbreaks and trends. Examples of such health conditions include chronic and communicable disease, injury and environment-mediated adverse incidents. DISCUSSION: Key advantages of ARTSSN over traditional paper-based methods are its timeliness, comprehensiveness and automation. Public health surveillance of communicable disease, injury, environmental hazard exposure and chronic disease now occurs in a single system in real time year round. Examples are given to demonstrate the public health value of this system, particularly during Pandemic (H1N1) 2009.

Entities:  

Mesh:

Year:  2010        PMID: 21370780

Source DB:  PubMed          Journal:  Can J Public Health        ISSN: 0008-4263


  8 in total

1.  Characterization of enterovirus activity, including that of enterovirus D68, in pediatric patients in Alberta, Canada, in 2014.

Authors:  Steven J Drews; Kimberley Simmonds; Hussain R Usman; Karen Yee; Sumana Fathima; Graham Tipples; Raymond Tellier; Kanti Pabbaraju; Sallene Wong; James Talbot
Journal:  J Clin Microbiol       Date:  2015-01-14       Impact factor: 5.948

2.  National and Regional Representativeness of Hospital Emergency Department Visit Data in the National Syndromic Surveillance Program, United States, 2014.

Authors:  Ralph J Coates; Alejandro Pérez; Atar Baer; Hong Zhou; Roseanne English; Michael Coletta; Achintya Dey
Journal:  Disaster Med Public Health Prep       Date:  2016-02-17       Impact factor: 1.385

Review 3.  Public health delivery in the information age: the role of informatics and technology.

Authors:  F Williams; A Oke; I Zachary
Journal:  Perspect Public Health       Date:  2019-02-13

4.  Perceived usefulness of syndromic surveillance in Ontario during the H1N1 pandemic.

Authors:  Rachel Savage; Anna Chu; Laura C Rosella; Natasha S Crowcroft; Monali Varia; Michelle E Policarpio; Norman G Vinson; Anne-Luise Winter; Karen Hay; Richard F Davies; Ian Gemmill; Don Willison; Ian Johnson
Journal:  J Public Health (Oxf)       Date:  2011-12-22       Impact factor: 2.341

5.  A review of the role of public health informatics in healthcare.

Authors:  Hassan A Aziz
Journal:  J Taibah Univ Med Sci       Date:  2016-10-11

Review 6.  The past, present, and future of public health surveillance.

Authors:  Bernard C K Choi
Journal:  Scientifica (Cairo)       Date:  2012-08-05

7.  Influenza-like illness-related emergency department visits: Christmas and New Year holiday peaks and relationships with laboratory-confirmed respiratory virus detections, Edmonton, Alberta, 2004-2014.

Authors:  Leah J Martin; Cindy Im; Huiru Dong; Bonita E Lee; James Talbot; David P Meurer; Shamir N Mukhi; Steven J Drews; Yutaka Yasui
Journal:  Influenza Other Respir Viruses       Date:  2016-08-30       Impact factor: 4.380

8.  Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data.

Authors:  L J Martin; H Dong; Q Liu; J Talbot; W Qiu; Y Yasui
Journal:  Epidemiol Infect       Date:  2019-12-02       Impact factor: 2.451

  8 in total

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