ISDS Conference

The Real-time Outbreak and Disease Surveillance system collects chief complaints as free text and uses a naïve Bayesian classifier called CoCo to classify the complaints into syndromic categories. CoCo 3.0 has been trained on 28,990 manually clas-sified chief complaints. The free text chief com-plaints are challenging to work with, due to problems caused by linguistic variations such as synonyms, abbreviations, acronyms, truncations, concatenations, misspellings and typographic errors. Failure to correct these word variations may result in missed cases, thereby decreasing sensitivity of detection.

Objective

To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance.

In 2004, the Indiana State Department of Health (ISDH) contracted with the Regenstrief Institute to build an information exchange infrastructure to support the collection of surveillance data. This pilot program involves implementation of electronic reporting in 46 of the state’s 114 emergency departments. Chief complaint data are collected and analyzed to identify clusters of disease earlier than a diagnosis can be confirmed or the disease reported to the ISDH. The system utilized the chief complaint coder CoCo to map the chief complaints into one of eight syndromes. This evaluation was completed after one-third of the pilot facilities were operational.

Objective

This evaluation was conducted to determine if any pilot hospitals have operational practices that may affect the ability of the Public Health Emergency Surveillance System to accurately and efficiently identify clusters of infectious disease in Indiana.

While traditional means of surveillance by governments, multi-national agencies, and institutional networks assist in reporting and confirming infectious disease outbreaks, these formal sources of information are limited by their geographic coverage and timeliness of information flow. In contrast, rapid global reach of electronic communication has resulted in the advent of informal sources of information on outbreaks. Informal resources include discussion sites, online news media, individual and organization reports and even individual search records. The earliest descriptions of the severe acute respiratory syndrome outbreak in Guangdon Province, south China came from informal reports. However, system development to date has been geared toward knowledge management and strategies for interpreting these data are underdeveloped. There is a need to move from simple knowledge reorganization to an analytic approach for disseminating timely yet specific signals.

Objective

Internet-based resources such as discussion sites and online news sources have become invaluable sources for a new wave of surveillance systems. The WHO relies on these informal sources for about 65% of their outbreak investigations. Despite widespread use of unstructured information there has been little, if any, data evaluation.

Animals continue to be recognized as a potential source of surveillance data for detecting emerging infectious diseases, bioterrorism preparedness, pandemic influenza preparedness, and detection of other zoonotic diseases. Detection of disease outbreaks in animals remains mostly dependent upon systems that are disease specific and not very timely. Most zoonotic disease outbreaks are detected only after they have spread to humans. The use of syndromic surveillance methods (outbreak surveillance using pre-diagnostic data) in animals is a possible solution to these limitations. The authors examine microbiology orders from a veterinary diagnostics laboratory (VDL) as a possible data source for early outbreak detection. They establish the species representation in the data, quantify the potential gain in timeliness, and use a CuSum method to study counts of microorganisms, animal species, and specimen collection sites as potential early indicators of disease outbreaks. The results indicate that VDL microbiology orders might be a useful source of data for a surveillance system designed to detect outbreaks of disease in animals earlier than traditional reporting systems.

The goal of this project is to compare automated syn-dromic surveillance queries using raw chief complaints to those pre-processed with the Emergency Medical Text Processor (EMT-P) system.

Syndromic surveillance aims to decrease the time to detection of an outbreak compared to traditional surveillance methods. Emergency department (ED) syndromic surveillance systems vary in their methodology and complexity and are usually based on presenting chief complaints. Prior work in ED-based syndromic surveillance has shown conflicting results on agreement between chief complaint and discharge diagnosis, which may be syndrome-dependent. The use of ED discharge diagnosis may improve surveillance validity if it can be done in a timely fashion.

Objective 

The purpose of this study is to characterize the relationship of emergency department chief complaint and final primary ICD-9 diagnosis assigned at the time of emergency department disposition for patients with symptoms and/or ICD-9 codes associated with influenza like illness (ILI) using an electronic medical record.

This study aims to evaluate the sensitivity, specificity and Positive Predictive Value (PPV) of body temperature measurements > 100.5 žF in relationship to laboratory confirmation of influenza and other ILI pathogens.

Objective This presentation discusses the problem of detecting small-scale events in biosurveillance data that are relatively sparse in the sense that the median count of monitored time series values is zero. Research goals are to understand conditions when methods adapted for sparseness are warranted, to examine adaptations of control charts and other algorithms under these scenarios, and to compare the detection performance of these algorithms.

To determine the predictive abilities of systematic emergency room chief complaints for the detection of acute infectious diseases in the Winnipeg population.

To describe and evaluate the SOS Medecins Bordeaux syndromic surveillance system .