[show abstract][hide abstract] ABSTRACT: Emergency Department (ED) chief complaint (CC) data are key components of syndromic surveillance systems. However, it is difficult to use CC data because they are not standardized and contain varying semantic and lexical forms for the same concept. The purpose of this project was to revise a previously-developed text processor for pre-processing CC data specifically for syndromic surveillance and then evaluate it for acute respiratory illness surveillance to support decisions by public health epidemiologists. We evaluated the text processor accuracy and used the results to customize it for respiratory surveillance. We sampled 3,699 ED records from a population-based public health surveillance system. We found equal sensitivity, specificity, and positive and negative predictive value of syndrome queries of data processed through the text processor compared to a standard keyword method on raw, unprocessed data.
[show abstract][hide abstract] ABSTRACT: The sensitivity and specificity of syndrome definitions used in early event detection (EED) systems affect the usefulness of the system for end-users. The ability to calculate these values aids system designers in the refinement of syndrome definitions to better meet public health needs. Utilizing a stratified sampling method and expert review to create a gold standard dataset for the calculation of sensitivity and specificity, we describe how varying syndrome structure impacts these statistical parameters and discuss the relevance of this to outbreak detection and investigation.