Evaluating real-time syndromic surveillance signals from ambulatory care data in four states.
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.
Yih
W Katherine
WK
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
Deshpande
Swati
S
Fuller
Candace
C
Heisey-Grove
Dawn
D
Hsu
John
J
Kruskal
Benjamin A
BA
Kulldorff
Martin
M
Leach
Michael
M
Nordin
James
J
Patton-Levine
Jessie
J
Puga
Ella
E
Sherwood
Edward
E
Shui
Irene
I
Platt
Richard
R
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
MMWR Suppl. 2005 Aug 26;54:147-50
16177706
Am J Epidemiol. 2004 Feb 1;159(3):217-24
14742279
MMWR Suppl. 2005 Aug 26;54:175-80
16177711
MMWR Suppl. 2004 Sep 24;53:43-9
15714626
MMWR Surveill Summ. 2002 Oct 25;51(7):1-10
12418623
MMWR Suppl. 2005 Aug 26;54:151-6
16177707
PLoS Med. 2005 Mar;2(3):e59
15719066
J Urban Health. 2003 Jun;80(2 Suppl 1):i8-13
12791773
MMWR Suppl. 2005 Aug 26;54:157-62
16177708
MMWR Suppl. 2004 Sep 24;53:184-9
15717390
Emerg Infect Dis. 2004 May;10(5):858-64
15200820
BMC Public Health. 2006 Sep 19;6:235
16984658
Biosecur Bioterror. 2003;1(2):77-81
15040185
J Urban Health. 2003 Jun;80(2 Suppl 1):i25-31
12791776
MMWR Suppl. 2004 Sep 24;53:190-5
15717391
MMWR Recomm Rep. 2004 May 7;53(RR-5):1-11
15129191