Back to Annual Meeting Page		American Public Health Association 133rd Annual Meeting & Exposition December 10-14, 2005 Philadelphia, PA

Abstract #114574

Katie Fox, MS and Christopher M. Kippes, MS. Epidemiology and Surveillance, Cuyahoga County Board of Health, 5550 Venture Dr., Parma, OH 44130, 216-201-2001 ext 1612, kfox@ccbh.net

Background: In 2001, Ohio began using the Ohio Disease Reporting System (ODRS), to track reportable diseases. Currently, this system collects limited epidemiologic information (e.g. food history and other potential exposure pathway data). Additionally, a standard data system for efficiently tracking information related to general non-confirmed illness and food related complaints does not exist.

Objectives: 1) Develop a data system that combines information from reported cases of disease including epidemiologic data, along with general complaint data to allow for a more efficient method of identifying potential disease/outbreak clusters. 2) Identify at-risk populations or areas in need of intervention to prevent any given disease/outbreak from spreading.

Methods: Standard forms were developed to collect information on reportable diseases as well as general illness and food related complaints. A companion database was developed using CDC's EpiInfo software. Methods were developed to merge information across all data sources. Weekly output consisting of the following: 1) standard maps to assist with the identification of potential disease/illness clusters; 2) reports attempting to identify common food sources for enteric diseases/illnesses; and 3) syndromic maps based on symptoms from general illness reports; will be created.

Results: At the time of this abstract preliminary results are not available.

Conclusions: Establishing a standard, comprehensive data system that is based on free software is a powerful tool that can be used by many local health departments to assist with epidemiologic investigations. Partnering with surrounding health jurisdictions can increase the utility and lead to a stronger comprehensive approach to identify potential disease/illness clusters.

Learning Objectives: At the end of this session, the participant will be able to

• Describe the components of a comprehensive data system that can be used to identify potential disease/illness clusters;
• List different ways to analyze and present epidemiologic data for potential disease/illness clusters.

Presenting author's disclosure statement:

I wish to disclose that I have NO financial interests or other relationship with the manufactures of commercial products, suppliers of commercial services or commercial supporters.