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Related Concept Videos

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable temporal or...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Introduction to Epidemiology01:26

Introduction to Epidemiology

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Related Experiment Video

Updated: Jun 23, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

Using encounters versus episodes in syndromic surveillance.

I Jung1, M Kulldorff, K P Kleinman

  • 1Department of Epidemiology and Biostatistics, The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr Mail Code 7933, San Antonio, TX, USA. jungi@uthscsa.edu

Journal of Public Health (Oxford, England)
|May 16, 2009
PubMed
Summary

Automated syndromic surveillance using all electronic health records may generate too many signals or miss outbreaks. Analyzing new illness episodes instead of repeat visits improves infectious disease outbreak detection accuracy.

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Last Updated: Jun 23, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
15:00

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

Area of Science:

  • Public Health
  • Epidemiology
  • Health Informatics

Background:

  • Automated electronic medical records (EMRs) are valuable tools for public health surveillance, enabling rapid detection of infectious disease outbreaks.
  • Current syndromic surveillance systems vary in their approach, with some including all patient encounters and others excluding repeat visits within a short timeframe to represent distinct illness episodes.

Purpose of the Study:

  • To evaluate the impact of including all patient encounters versus excluding repeat encounters on the detection of disease signals in syndromic surveillance.
  • To compare the performance of different definitions of 'new episodes' (2, 6, or 12 weeks) in identifying outbreaks.

Main Methods:

  • The prospective space-time permutation scan statistic was employed for daily analysis.
  • Two approaches were compared: analyzing all encounters and analyzing episodes defined as new within 2, 6, or 12 weeks.
  • Data from a Massachusetts Health Maintenance Organization (HMO) in 1999 for four distinct syndromes (lower respiratory, lower gastrointestinal, upper gastrointestinal, neurologic) were utilized.

Main Results:

  • Significant discrepancies in detected signals were observed between the two methods.
  • Using all encounters yielded substantially more signals (70, 68, 21, 15) compared to using new episodes (15-20, 3, 4-5, 0) across the four syndromes.
  • The choice of episode definition (2, 6, or 12 weeks) also impacted signal detection rates.

Conclusions:

  • Including all encounters in syndromic surveillance may lead to an overabundance of signals and potentially mask true outbreak anomalies.
  • Conversely, defining illness episodes can also result in missed signals.
  • Careful consideration of encounter inclusion/exclusion criteria is crucial for optimizing syndromic surveillance accuracy.