Recorded presentation

John O. Davies-Cole, PhD, MPH¹, Michael A. Stoto, PhD², Aaron Adade, PhD¹, Kerda DeHaan, MS³, Garret Lum, MPH⁴, and A. Chevelle Glymph, MPH⁵. (1) Bureau of Epidemiology and Health Risk Assessment, District of Columbia Department of Health, 825 North Capitol St NE, Washington, DC 20002, 202-442-9138, john.davies-cole@dc.gov, (2) Statistics group and RAND Health, RAND, 1200 South Hayes St., Arlington, VA 22202-5050, (3) Department of Health, District of Columbia Department of Health, 825 North Capitol St NE, Washington, DC 20002, (4) Bureau of Epidemiology and Health Risk Assessment, Department of Health, 825 North Capitol St NE, Washington, DC 20002, (5) Bureau of Epidemiology & Health Risk Assessment, District of Columbia Department of Health, 825 North Capitol St NE, #3142, Washington, DC 20002

Background: Hospital, laboratory and clinical data have been collected by health departments for many years. New sources of data or “non-traditional data” like poison control and EMS data are now being collected to aid in detecting unusual occurrences due to bioterrorism. However, very little is known of the value of using these kinds of data in detecting a disease outbreak or an agent of bioterrorism.

Objective: To evaluate poison control and EMS data and assess their sensitivity in detecting a possible event of bioterrorism or other disease outbreaks.

Methods: The new Washington DC Automated Disease Surveillance System (WADSS) extracts data from the Poison Control Access Database via an HTTP post of the data elements every 5 minutes. EMS data is pulled chronologically from the EMS Server using file transfer protocol (FTP) across a virtual private network (VPN) every 5 minutes. Poison control and EMS data will be analyzed using SAS and the data will be tested using simulated disease patterns. Data flow through the WADSS, and the sensitivity of the data in detecting unusual events will be described. Time series analysis of the data and incident clusters in both time and space will be presented.

Conclusion: The use of non traditional data like poison control and EMS data will illustrate the potential of using non traditional data that may be more timely than traditional medical data in detecting disease outbreaks or events of bioterrorism.

Learning Objectives:

• Understand the difference between traditional and non-traditional data for disease surveillance and agents of bioterrorism
• Understand how traditional data are electronic transmitted to the District of Columbia Automated Disease Surveillance System (WADSS)
• Evaluate the potential of using traditional data for detecting agents of bioterrorism or other disease outbreaks

Presenting author's disclosure statement:
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.

Recorded presentation