Document Detail


Use of automated ambulatory-care encounter records for detection of acute illness clusters, including potential bioterrorism events.
MedLine Citation:
PMID:  12141958     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The advent of domestic bioterrorism has emphasized the need for enhanced detection of clusters of acute illness. We describe a monitoring system operational in eastern Massachusetts, based on diagnoses obtained from electronic records of ambulatory-care encounters. Within 24 hours, ambulatory and telephone encounters recording patients with diagnoses of interest are identified and merged into major syndrome groups. Counts of new episodes of illness, rates calculated from health insurance records, and estimates of the probability of observing at least this number of new episodes are reported for syndrome surveillance. Census tracts with unusually large counts are identified by comparing observed with expected syndrome frequencies. During 1996-1999, weekly counts of new cases of lower respiratory syndrome were highly correlated with weekly hospital admissions. This system complements emergency room- and hospital-based surveillance by adding the capacity to rapidly identify clusters of illness, including potential bioterrorism events.
Authors:
Ross Lazarus; Ken Kleinman; Inna Dashevsky; Courtney Adams; Patricia Kludt; Alfred DeMaria; Richard Platt
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Emerging infectious diseases     Volume:  8     ISSN:  1080-6040     ISO Abbreviation:  Emerging Infect. Dis.     Publication Date:  2002 Aug 
Date Detail:
Created Date:  2002-07-26     Completed Date:  2002-09-18     Revised Date:  2013-06-09    
Medline Journal Info:
Nlm Unique ID:  9508155     Medline TA:  Emerg Infect Dis     Country:  United States    
Other Details:
Languages:  eng     Pagination:  753-60     Citation Subset:  IM    
Affiliation:
Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. rerla@channing.harvard.edu
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MeSH Terms
Descriptor/Qualifier:
Acute Disease / epidemiology
Ambulatory Care / statistics & numerical data*
Bioterrorism / statistics & numerical data*
Cluster Analysis
Disease Outbreaks / statistics & numerical data*
Humans
Massachusetts / epidemiology
Medical Informatics Applications*
Medical Records Systems, Computerized*
Population Surveillance / methods*
Reproducibility of Results
Respiratory Tract Infections / epidemiology
Grant Support
ID/Acronym/Agency:
U90/CCU116997/CC/ODCDC CDC HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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