Syndromic Surveillance

NC DETECT receives data on at least a daily basis from five data sources: emergency departments (ED), the statewide poison center (CPC), the statewide EMS data collection system, a regional wildlife center and laboratories from the NC State College of Veterinary Medicine.  A Web portal is available to users at state, regional and local levels and provides syndromic surveillance reports as well as reports for broader public health surveillance such as injury, occupational health, and post-disaster.  The current portal is built on access controls initially designed in 2002 for hospital-based users only.  The role-based access was modified slightly in 2004 to accommodate public health epidemiologists (PHEs) at the local, regional and state levels who wanted county-based report access.  The design used, however, was shortsighted and limited.  For example, the controls cannot accommodate certain users’ access to non-ED data sources as well as the ability to retrieve protected health information (PHI) via the portal when needed for investigation.  These evolving user needs have led to a full system redesign with a much more robust security model.

Objective

This paper describes the role-based access used in the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) Web portal for early event detection and timely public health surveillance.

This abstract describes Missouriís experience with syndromic surveillance. Missouri has expanded from acquiring pre-tabulated data from volunteers to receiving patient-level data via electronic feeds from 85 hospitals across the state processed through multiple analysis, visualization, and reporting tools. Missouri and its partners use these data for early event detection and situational awareness at the state and local levels.

This paper presents an investigation using data mining techniques to model patterns of influenza from positive case demographics, symptoms and laboratory tests.

This paper describes a novel approach to the statistical classification of free-text chief complaints for the purpose of syndromic surveillance.

The primary objective of this study is to assess the capability of an advanced text analytics tool that uses natural language processing techniques to extract important medical information collected as part of routine emergency room care (history, symptoms, vital signs, test results, initial diagnosis, etc.). This information will be automatically, accurately, and efficiently converted from unstructured text into use-able information, which can then be used to identify cases that are the result of a naturally occurring outbreak or bioterrorism event. This information would then be available to (1) communicate to the treating physician, and (2) message back to organizations aggregating data at a higher level, such as the Centers for Disease Control and Prevention (CDC) and the Department of Homeland Security (DHS).

This paper describes a cluster of influenza-like illness (ILI) prospectively identified through emergency department (ED) syndromic surveillance (SS).

In the spring of 2005, the ISDH began using Electronic Surveillance System for the Early Notification of Community-based Epidemics  (ESSENCE) application to analyze emergency department (ED) chief complaint data for syndromic surveillance purposes.  While granting hospitals and local health departments access to their data through ESSENCE has been desirable since the start of the PHESS project, an aggressive timeline made it necessary to direct all resource capacity toward first establishing hospital ED data connections.  The Marion County Health Department (Indianapolis) was the only LHD in the state with access to its 14 hospitals through ESSENCE.

However, because hospitals and local health departments (except Marion County) did not have access to their data through ESSENCE, any syndromic alert follow-up conducted by the ISDH was accomplished primarily by telephone.   This method, while feasible, was inefficient.  The ISDH felt that alert data follow-up could be greatly facilitated if hospitals and LHDs could view these data through ESSENCE just as the ISDH was doing.

Objective

This paper describes how the Indiana State Department of Health (ISDH) improved response capability by increasing local health department (LHD) and hospital access to syndromic surveillance data as part of the stateís evolving Public Health Emergency Surveillance System (PHESS).

In order to assess the use of rabies post-exposure prophylaxis in Indiana, the Communicable Disease Reporting Rule, adopted October 11, 2000, requires the reporting of rabies PEP administration.

Indiana is a “home rule” state; that is to say, local (county) health departments (LHD) are responsible for health issues within their jurisdiction.  Reportable diseases are passively reported to the ISDH through local health department by hospitals and physicians.   Often this results in under-reporting of things such as rabies PEP.

While the primary purpose of the PHESS is to enable early detection of acts of bioterrorism, naturally occurring outbreaks, and as a situational awareness tool, PHESS staff have continually worked to find other practical public health applications for the syndromic data.  The Epidemiology Resource Center at the ISDH houses subject matter experts in many areas of public health, including veterinary epidemiology.  Until fall of 2006, the veterinary epidemiologist received all reports of rabies PEP via hard copy.

Objective

The purpose of this paper is to describe how the Indiana State Department of Health (ISDH) leverages syndromic surveillance data to improve statewide rabies post-exposure prophylaxis (PEP) reporting by hospitals. The Public Health Emergency Surveillance System (PHESS) is Indianaís syndromic surveillance system and resides at the ISDH.

An N-gram is a sub-sequence of n items from a given sequence where n can be 1, 2,…, n and the items can be letters or words. N-gram models are widely used in statistical natural language processing [3]. In the syndromic surveillance context, N-grams can be used to cluster or classify natural language data.  They can also help in the design of kernels for machine learning algorithms such as support vector machines to learn from text data.  This work calculates the similarity percentages of ED or TH reasons to syndromic fingerprints using Ngrams. We define “reasons similarity” as the percentage of matched N-grams derived from the reasons field of an ED or TH record with the fingerprint of a syndrome. The fingerprint of a syndrome is a list of frequent N-grams related to this syndrome.  This fingerprint is constructed by collecting a large sample of classified reasons data for a particular syndrome, calculating all of the N-grams for this set and then selecting the most frequent N-grams to form a profile or fingerprint. N-gram generation may require extensive processing time especially for large files but this issue has been addressed by using parallel computation.

Objective

The objective of this work is to identify syndromic fingerprints in reasons for entering an emergency department (ED) or calling telehealth (TH). It also demonstrates that these fingerprints are valuable for classification.

The existing New York State Department of Health emergency department syndromic surveillance system has used patient’s chief complaint (CC) for assigning to six syndrome categories (Respiratory, Fever, Gastrointestinal, Neurological, Rash, Asthma). The sensitivity and specificity of the CC computer algorithms that assign CC to syndrome categories are determined by using chart review as the criterion standard. These analyses are used to refine the algorithm and to evaluate the effect of changes in the syndrome definitions. However, the chart review (CR) method is labor intensive and expensive. Using an automated ICD9 code-based assignment as a surrogate for chart review could offer a significant cost reduction in this process and allow us to survey a much larger sample of visits.