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Deciphering data anomalies in BioSense.

Leslie Z Sokolow1, N Grady, H Rolka

  • 1Innovative Emergency Management, Inc., Atlanta, Georgia, USA. lsokolow@cdc.gov

MMWR Supplements
|September 24, 2005
PubMed
Summary
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CDC analysts monitored nationwide syndromic data using the BioSense application. While no disease outbreaks were detected, the system identified data anomalies requiring rapid analysis for public health surveillance.

Area of Science:

  • Public Health Surveillance
  • Epidemiology
  • Health Informatics

Background:

  • The Centers for Disease Control and Prevention (CDC) has utilized the BioSense surveillance application since June 2004 for daily nationwide syndromic data monitoring.
  • A dedicated team of CDC analysts oversees the BioSense application, playing a crucial role in identifying and interpreting data anomalies.

Purpose of the Study:

  • To examine the role of CDC analysts in identifying and deciphering data anomalies within the BioSense surveillance application.
  • To discuss the limitations of the current surveillance system, lessons learned, and propose future improvements for national syndromic surveillance methodology.

Main Methods:

  • Data integration from diverse sources including clinical diagnoses (ICD-9-CM), medical procedures (CPT codes), over-the-counter product sales, and laboratory tests.

Related Experiment Videos

  • Filtering of all data to exclude irrelevant information for syndromic surveillance purposes.
  • Utilized statistical algorithms and analytical visualization features for anomaly detection.

    • Main Results:

    • During June-November 2004, approximately 160 data anomalies were examined, with no disease outbreaks or deliberate pathogen exposures detected.
    • Anomalies primarily involved unusual changes in daily data volume or types of clinical diagnoses and procedures.
    • Routine monitoring steps include anomaly detection, scope estimation, supplemental fact-gathering, multi-source data comparison, hypothesis development, and validation, requiring rapid completion for early detection.

    Conclusions:

    • The BioSense system necessitates an empirical learning process to optimize the use of public health data.
    • Enhancing the user interface and incorporating input from local public health partners can improve the effectiveness of syndromic surveillance.