OBJECTIVES: To determine the effectiveness of a simulated @entity341 ( @entity341 )-based surveillance system to detect @entity166 ( @entity166 ) occurrences in the community. METHODS: Medical records of @entity1 presenting to an urban @entity341 between January 1, 1999, and December 31, 2000, were retrospectively reviewed for ICD-9 codes related to @entity166 symptomatology. ICD-9 codes, categorized into viral, gastrointestinal, skin, @entity149 (CNS), or pulmonary symptom clusters, were correlated with reportable @entity166 identified by the local @entity283 ( @entity283 ). These reportable @entity166 are designated @entity66 ( @entity66 ) by the Ohio Department of Health. Cross-correlation functions (CCFs) tested the temporal relationship between @entity341 symptom presentation and @entity283 identification of @entity66 . The 95% confidence interval for lack of trend correlation was 0.0 +/- 0.074; thus CCFs > 0.074 were considered significant for trend correlation. Further cross-correlation analysis was performed after chronic and non-community-acquirable @entity166 were removed from the @entity283 database as a model for bioterrorism surveillance. RESULTS: Fifteen thousand five hundred sixty-nine @entity341 @entity1 and 6,489 @entity283 @entity1 were identified. Six thousand two hundred eight occurrences of true @entity66 were identified. Only 87 (1.33%) @entity283 cases were processed on weekends. During the study period, increased @entity341 symptom presentation preceded increased @entity283 identification of respective @entity66 by 24 hours for all symptom clusters combined (CCF = 0.112), @entity344 (CCF = 0.084), @entity358 (CCF = 0.110), and CNS symptoms (CCF = 0.125). The bioterrorism surveillance model revealed increased @entity341 symptom presentation continued to precede increased @entity283 identification of the respective @entity66 by 24 hours for all symptom clusters combined (CCF = 0.080), @entity358 (CCF = 0.100), and CNS symptoms (CCF = 0.120). CONCLUSIONS: Surveillance of @entity341 symptom presentation has the potential to identify clinically important @entity166 occurrences in the community 24 hours prior to @entity283 identification. Lack of weekend @entity283 data collection suggests that the @entity341 is a more appropriate setting for real-time @entity166 surveillance.

Surveillance of @entity166 occurrences in the community: an analysis of symptom presentation in the XXXX .

@entity1 :: ('9606', 'Species') :: ['patients'] @entity66 :: ('MESH:D004194', 'Disease') :: ['CADs', 'class A diseases'] @entity283 :: ('OMIM:603663', 'Disease') :: ['HD', 'health department'] @entity341 :: ('MESH:D004630', 'Disease') :: ['emergency department', 'ED'] @entity149 :: ('MESH:D002493', 'Disease') :: ['fever, central nervous system'] @entity344 :: ('MESH:D005767', 'Disease') :: ['gastrointestinal symptoms'] @entity358 :: ('MESH:D008171', 'Disease') :: ['pulmonary symptoms'] @entity166 :: ('MESH:D003141', 'Disease') :: ['infectious diseases', 'ID', 'infectious disease']
@entity341 :: (MESH:D004630,Disease) :: ['emergency department']
