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Syndromic surveillance systems have long been an important part of the public health arena. The long standing goal of early detection of disease outbreak has gained new urgency and requires a broader spectrum in the era of potential bioterrorism. A number of programs have used syndromic surveillance to broadly monitor community health. Outpatient chief complaints as well as positive laboratory tests have been used to monitor the occurrence of natural diseases. 

Limitations of the systems currently attempted include overbroad syndromic categories, labor intensive syndrome recognition training and time intensive manual data entry. Optimal use of laboratory data has been impeded by some of the same issues as well as a too often narrow focus and significant limitations on real time reporting. Given the likelihood of blunt and/or penetrating trauma being a manifestation of terrorist activity, the continuous inclusion of common traumatic and medical emergency conditions is a valuable tool for surveillance.

 

Objective

This paper describes the use of a multiple collective community health care database to monitor the occurrence of natural and manmade illness and injuries.

Referenced File
Submitted by elamb on

Rhode Island implemented the Real-time Outbreak and Disease Surveillance (RODS) system, developed in 1999 by the University of Pittsburgh’s Center for Biomedical Informatics. This system is based on real-time information from hospital emergency departments that is transmitted and analyzed electronically for the purpose of early detection of and situational awareness for public health emergencies. Through this system, chief complaint is reported in real-time. Diagnoses, coded in the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), are reported to the RI RODS system as they become available. Three hospitals are currently participating in a pilot implementation of the RI RODS system.

Preliminary work by a CDC Working Group (CDCWG) developed recommendations for syndrome definitions for use in syndromic surveillance programs. Ten syndromes, based on ICD-9-CM diagnosis codes, identified diseases associated with critical bioterrorism-associated agents or indicative of naturally occurring infectious disease outbreaks. As a component of the evaluation of the RI RODS system, we evaluated the RI RODS chief complaint classifier (CoCo) using ICD-9-CM codes and the CDCWG work as the gold standard.

 

Objective

This paper presents findings related to the evaluation of the CoCo used in the pilot implementation of a syndromic surveillance system in Rhode Island.

Referenced File
Submitted by elamb on

In the United States, 800,000-1.4 million people are chronically infected with hepatitis B virus (HBV); these persons are at increased risk for chronic liver disease and its sequelae. Current national viral hepatitis surveillance is a passive laboratory-initiated reporting system to state or local health departments with only 39 health departments reporting chronic HBV infection in the National Notifiable Disease Surveillance System. Since active HBV surveillance can be expensive and labor-intensive, the ICD-9 coding system has been proposed for surveillance of chronic hepatitis B.

 

Objective

To evaluate the sensitivity, specificity, positive and negative predictive values of the ICD-9 coding system for surveillance of chronic hepatitis B virus infection (HBV) using data from an observational cohort study in which ICD-9-coded HBV cases were validated by chart review

Submitted by hparton on

Comprehensive medical syndrome definitions are critical for outbreak investigation, disease trend monitoring, and public health surveillance. However, because current definitions are based on keyword string-matching, they may miss important distributional information in free text and medical codes that could be used to build a more general classifier. Here, we explore the idea that individual ICD codes can be categorized by examining their contextual relationships across all other ICD codes. We extend previous work in representation learning with medical data by generating dense vector embeddings of these ICD codes found in emergency department (ED) visit records. The resulting representations capture information about disease co-occurrence that would typically require SME involvement and support the development of more robust syndrome definitions.

Objective:

To better define and automate biosurveillance syndrome categorization using modern unsupervised vector embedding techniques.

Submitted by elamb on
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