Automated monitoring of clusters of falls associated with severe winter weather using the BioSense system.
OBJECTIVE
To identify and characterise clusters of emergency department (ED) visits for fall injuries during the 2007-2008 winter season.


METHODS
Hospital ED chief complaints and diagnoses from hospitals reporting to the Centers for Disease Control and Prevention BioSense system were analysed. The authors performed descriptive analyses, used time series charts on data aggregated by metropolitan statistical areas (MSAs), and used SaTScan to find spatial-temporal clusters of visits from falls.


RESULTS
In 2007-2008, 17 clusters of falls in 13 MSAs were found; the median number of excess ED visits for falls was 71 per day. SaTScan identified 11 clusters of falls, of which seven corresponded to MSA clusters found by time series and five included more than one state/district. Most clusters coincided with known periods of snowfall or freezing rain.


CONCLUSIONS
The results show the role that a national automated system can play in tracking widespread injuries. Such a system could be harnessed to assist with prevention strategies.
Dey
Achintya N
AN
Centers for Disease Control and Prevention, National Center for Public Health Informatics, Atlanta, Georgia 30333, USA. adey@cdc.gov
Hicks
Peter
P
Benoit
Stephen
S
Tokars
Jerome I
JI
D000058:Accidental Falls / Q000517:prevention & control / Q000706:statistics & numerical data*; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011891:Rain; D018570:Risk Assessment; D012621:Seasons; D018571:Sentinel Surveillance; D017678:Sex Distribution; D012914:Snow; D014481:United States / Q000453:epidemiology; D014947:Wounds and Injuries / Q000453:epidemiology* / Q000517:prevention & control