Usefulness of syndromic data sources for investigating morbidity resulting from a severe weather event

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.