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BACKGROUND: Developed in 2001 by Los Angeles County Department of Medical Examiner-Coroner (DME-C) and Los Angeles County Department of Public Health (LAC-DPH), the Unusual Death Surveillance System (UDSS) functions to identify potential acts of bioterrorism and emerging infectious disease events in LAC. DME-C provides a 42-variable dataset for coroner cases on a daily basis, and includes all cases reported in the previous 24 hours. The dataset contains information on demographics, the circumstances of the death, as well as description of the death scene. We assessed the effect of system automation by reviewing data from two time periods.
METHODS: Previously coroner reports were manually queried by DME-C staff and emailed to LAC-DPH. Automation of data exchange, completed in July 2012, consisted of querying data directly from the DME-C server and placing in a secure FTP folder location. A daily program that imported data into SAS and ran an algorithm performing text string searches for infectious disease related key words was also automated. Additionally, datasets are reviewed by LAC-DPH staff. Total number of coroner cases reported, flagged for suspected infectious cause of death, followed-up, and designated as reportable disease death related for the year before automation (July 2011-June2012) and after automation (July 2012-June 2013) were compared.
RESULTS: 16,108 coroner cases were reported in the year before automation, and 17,702 in the year after. There was a 149% increase in the number of cases flagged by SAS, from 518 in the year before automation to 1293 in the year after. This lead to a 26% increase in cases that were followed for final cause of death from 381 in the year before automation to 480 in the year after, as well as a 49% increase (35 to 52) in reportable disease deaths identified using UDSS. A 13% increase in the number of cases with active follow up with the DME-C from 128 to 145 was also noted. Work-time saved in the first year of automation is estimated to be ~50 hours from DME-C staff and ~200 hours from LAC-DPH staff.
CONCLUSIONS: After automation, complete death description and synopsis variables became readily available; this allowed for better analysis of death circumstances and more active follow-up. Also, automation allowed for analysis to be completed whether or not assigned LAC-DPH staff is available to generate the analysis. This process also improved communication between LAC-DPH and DME-C, which continues to be valuable in the response to a potential bioterrorism event.