Literature DB >> 19443438

Using encounters versus episodes in syndromic surveillance.

I Jung1, M Kulldorff, K P Kleinman, W K Yih, R Platt.   

Abstract

BACKGROUND: Automated electronic medical records may be useful for syndromic surveillance to quickly detect infectious disease outbreaks. Some syndromic surveillance systems include every encounter in the analysis, whereas others exclude individuals' repeat encounters within the same syndrome occurring within a short period of time, with the rationale that these represent follow-up visits rather than new episodes of illness.
METHODS: We evaluate the effect of keeping all encounters as compared with removing repeat encounters. Using the prospective space-time permutation scan statistic, we performed daily analyses on all encounters versus on episodes defined as encounters new within 2, 6 or 12 weeks. Data were taken from a Massachusetts Health Maintenance Organization (HMO) for the calendar year 1999 for four different syndromes.
RESULTS: We found extensive disagreement in the number of signals detected: 70, 68, 21 and 15 signals when using all encounters versus 15-20, 3, 4-5 and 0 signals when using only new episodes for lower respiratory, lower gastrointestinal, upper gastrointestinal and neurologic syndromes, respectively.
CONCLUSION: Using all encounters in syndromic surveillance may not only create too many signals but may also miss some signals by masking the anomalies generated by actual episodes. However, it is also possible to miss signals when using episodes.

Entities:  

Mesh:

Year:  2009        PMID: 19443438      PMCID: PMC2781720          DOI: 10.1093/pubmed/fdp040

Source DB:  PubMed          Journal:  J Public Health (Oxf)        ISSN: 1741-3842            Impact factor:   2.341


  26 in total

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4.  Syndromic analysis of computerized emergency department patients' chief complaints: an opportunity for bioterrorism and influenza surveillance.

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6.  Disease outbreak detection system using syndromic data in the greater Washington DC area.

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Authors:  R Lazarus; K P Kleinman; I Dashevsky; A DeMaria; R Platt
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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
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3.  Maximum linkage space-time permutation scan statistics for disease outbreak detection.

Authors:  Marcelo A Costa; Martin Kulldorff
Journal:  Int J Health Geogr       Date:  2014-06-10       Impact factor: 3.918

4.  Spatial patterns of epilepsy-related emergency department visits in california.

Authors:  Jim E Banta; Askari Addison; W Lawrence Beeson
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