title: Evaluating real-time syndromic surveillance signals from ambulatory care data in four states.
abstract: OBJECTIVE
  We evaluated a real-time ambulatory care-based syndromic surveillance system in four metropolitan areas of the United States.
  
  
  METHODS
  Health-care organizations and health departments in California, Massachusetts, Minnesota, and Texas participated during 2007-2008. Syndromes were defined using International Classification of Diseases, Ninth Revision diagnostic codes in electronic medical records. Health-care organizations transmitted daily counts of new episodes of illness by syndrome, date, and patient zip code. A space-time permutation scan statistic was used to detect unusual clustering. Health departments followed up on e-mailed alerts. Distinct sets of related alerts ("signals") were compared with known outbreaks or clusters found using traditional surveillance.
  
  
  RESULTS
  The 62 alerts generated corresponded to 17 distinct signals of a potential outbreak. The signals had a median of eight cases (range: 3-106), seven zip code areas (range: 1-88), and seven days (range: 3-14). Two signals resulted from true clusters of varicella; six were plausible but unconfirmed indications of disease clusters, six were considered spurious, and three were not investigated. The median investigation time per signal by health departments was 50 minutes (range: 0-8 hours). Traditional surveillance picked up 124 clusters of illness in the same period, with a median of six ill per cluster (range: 2-75). None was related to syndromic signals.
  
  
  CONCLUSIONS
  The system was able to detect two true clusters of illness, but none was of public health interest. Possibly due to limited population coverage, the system did not detect any of 124 known clusters, many of which were small. The number of false alarms was reasonable.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Ave., 6th Floor, Boston, MA 02215, USA. katherine_yih@harvardpilgrim.org
authors.forename: W Katherine
authors.initials: WK
authors.lastname: Yih
authors.forename: Swati
authors.initials: S
authors.lastname: Deshpande
authors.forename: Candace
authors.initials: C
authors.lastname: Fuller
authors.forename: Dawn
authors.initials: D
authors.lastname: Heisey-Grove
authors.forename: John
authors.initials: J
authors.lastname: Hsu
authors.forename: Benjamin A
authors.initials: BA
authors.lastname: Kruskal
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
authors.forename: Michael
authors.initials: M
authors.lastname: Leach
authors.forename: James
authors.initials: J
authors.lastname: Nordin
authors.forename: Jessie
authors.initials: J
authors.lastname: Patton-Levine
authors.forename: Ella
authors.initials: E
authors.lastname: Puga
authors.forename: Edward
authors.initials: E
authors.lastname: Sherwood
authors.forename: Irene
authors.initials: I
authors.lastname: Shui
authors.forename: Richard
authors.initials: R
authors.lastname: Platt
mesh_terms: D000553:Ambulatory Care / Q000706:statistics & numerical data*; D001900:Boston / Q000453:epidemiology; D002140:California / Q000453:epidemiology; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D006801:Humans; D008910:Minnesota / Q000453:epidemiology; D040442:Public Health Informatics / Q000379:methods*; D018571:Sentinel Surveillance; D013029:Space-Time Clustering; D013577:Syndrome; D013781:Texas / Q000453:epidemiology; D014504:Urban Health / Q000706:statistics & numerical data
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