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Usefulness of syndromic data sources for investigating morbidity resulting from a severe weather event.
MedLine Citation:
PMID:  21402825     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Objective:  We evaluated emergency department (ED) data, emergency medical services (EMS) data, and public utilities data for describing an outbreak of carbon monoxide (CO) poisoning following a windstorm. Methods:  Syndromic ED data were matched against previously collected chart abstraction data. We ran detection algorithms on selected time series derived from all 3 data sources to identify health events associated with the CO poisoning outbreak. We used spatial and spatiotemporal scan statistics to identify geographic areas that were most heavily affected by the CO poisoning event. Results:  Of the 241 CO cases confirmed by chart review, 190 (78.8%) were identified in the syndromic surveillance data as exact matches. Records from the ED and EMS data detected an increase in CO-consistent syndromes after the storm. The ED data identified significant clusters of CO-consistent syndromes, including zip codes that had widespread power outages. Weak temporal gastrointestinal (GI) signals, possibly resulting from ingestion of food spoiled by lack of refrigeration, were detected in the ED data but not in the EMS data. Spatial clustering of GI-based groupings in the ED data was not detected. Conclusions:  Data from this evaluation support the value of ED data for surveillance after natural disasters. Enhanced EMS data may be useful for monitoring a CO poisoning event, if these data are available to the health department promptly.
Authors:
Atar Baer; Yevgeniy Elbert; Howard S Burkom; Rekha Holtry; Joseph S Lombardo; Jeffrey S Duchin
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Publication Detail:
Type:  Journal Article     Date:  2010-09-28
Journal Detail:
Title:  Disaster medicine and public health preparedness     Volume:  5     ISSN:  1938-744X     ISO Abbreviation:  Disaster Med Public Health Prep     Publication Date:  2011 Mar 
Date Detail:
Created Date:  2011-03-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101297401     Medline TA:  Disaster Med Public Health Prep     Country:  United States    
Other Details:
Languages:  eng     Pagination:  37-45     Citation Subset:  IM    
Affiliation:
Atar Baer is with Public Health-Seattle and King County, and the University of Washington Department of Epidemiology. Yevgeniy Elbert, Howard S. Burkom, Rekha Holtry, and Joseph S. Lombardo are with Johns Hopkins University-Applied Physics Laboratory. Jeffrey S. Duchin is with Public Health-Seattle and King County, and the University of Washington Department of Epidemiology.
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