Literature DB >> 16177705

Syndromic surveillance on the epidemiologist's desktop: making sense of much data.

Kathy J Hurt-Mullen1, J Coberly.   

Abstract

INTRODUCTION: Syndromic surveillance systems are becoming increasingly common in health departments. These systems represent a substantial improvement in the timeliness of ascertainment of community health status. For the value of such systems to be realized, protocols are needed for review and analysis of the findings that these systems produce.
METHODS: A workgroup of experienced syndromic surveillance users and developers was convened to discuss approaches to data review and analyses. The discussion was structured to include general principles of the use of syndromic surveillance; how and why specific data are reviewed; integration of multiple data sources; daily versus research uses of systems; how data anomalies are identified by users and surveillance systems; the relative merits of anomalies; how a data anomaly is investigated to determine if it warrants a public health response; and how such a public health response should be framed.
RESULTS: From this discussion, a generalized and more detailed process was documented that describes the common elements of analysis used by the workgroup participants.
CONCLUSION: Establishment of a framework for evaluation and response to syndromic surveillance data will facilitate the implementation of these systems and standardization of procedures for validation of system findings. Careful development of an evaluation and response framework should be undertaken to assess whether use of syndromic surveillance systems requires excess work to distinguish between statistical anomalies and important public health events.

Entities:  

Mesh:

Year:  2005        PMID: 16177705

Source DB:  PubMed          Journal:  MMWR Suppl        ISSN: 2380-8942


  9 in total

1.  Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation.

Authors:  Fernanda C Dórea; Beverly J McEwen; W Bruce McNab; Crawford W Revie; Javier Sanchez
Journal:  J R Soc Interface       Date:  2013-04-10       Impact factor: 4.118

2.  Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data.

Authors:  Sharon K Greene; Martin Kulldorff; Jie Huang; Richard J Brand; Kenneth P Kleinman; John Hsu; Richard Platt
Journal:  Stat Med       Date:  2011-02-28       Impact factor: 2.373

3.  Combining free text and structured electronic medical record entries to detect acute respiratory infections.

Authors:  Sylvain DeLisle; Brett South; Jill A Anthony; Ericka Kalp; Adi Gundlapallli; Frank C Curriero; Greg E Glass; Matthew Samore; Trish M Perl
Journal:  PLoS One       Date:  2010-10-14       Impact factor: 3.240

Review 4.  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

5.  Spatial-temporal clustering of companion animal enteric syndrome: detection and investigation through the use of electronic medical records from participating private practices.

Authors:  R M Anholt; J Berezowski; C Robertson; C Stephen
Journal:  Epidemiol Infect       Date:  2014-12-29       Impact factor: 4.434

6.  Innovative uses for syndromic surveillance.

Authors:  Erin K O'Connell; Guoyan Zhang; Fermin Leguen; Anthoni Llau; Edhelene Rico
Journal:  Emerg Infect Dis       Date:  2010-04       Impact factor: 6.883

7.  Statistical analyses in disease surveillance systems.

Authors:  Andres G Lescano; Ria Purwita Larasati; Endang R Sedyaningsih; Khanthong Bounlu; Roger V Araujo-Castillo; Cesar V Munayco-Escate; Giselle Soto; C Cecilia Mundaca; David L Blazes
Journal:  BMC Proc       Date:  2008-11-14

8.  Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.

Authors:  Hongzhang Zheng; Holly Gaff; Gary Smith; Sylvain DeLisle
Journal:  PLoS One       Date:  2014-07-09       Impact factor: 3.240

9.  Can long-term historical data from electronic medical records improve surveillance for epidemics of acute respiratory infections? A systematic evaluation.

Authors:  Hongzhang Zheng; William H Woodall; Abigail L Carlson; Sylvain DeLisle
Journal:  PLoS One       Date:  2018-01-31       Impact factor: 3.240

  9 in total

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