An estimated 76 million cases of food borne disease illnesses, 325,000 hospitalizations and 5,000 deaths occur each year in the U. S. Although most food borne disease outbreaks (FBDOs) are related to naturally occurring events, they have also been produced by deliberate contamination. The CDC has directed public health departments to begin addressing bioterrorism preparedness efforts through improving food borne surveillance systems. The goal of this research is to provide specialized training in epidemiologic methods to prepare for a public health nursing research career. The purpose of this study is to develop a prediction algorithm that utilizes real-time (instantaneous) Poison Center data to detect and analyze FBDOs.
Research aims i nclude: a) compare historical FBDO rates in Pima County reported by Pima County Health Department (PCHD) and food borne illness cases collected by the Arizona Poison and Drug Information Center (AZPDIC) for 1998-2002; b) develop a prediction algorithm; c) validate the algorithm; and d) prospectively apply the algorithm. The research methodology involves retrospective and prospective analysis of existing PCHD and ZPDIC databases using time lag regression models. ? ?
