ISDS Conference

Reporting allows for the collection of statistics that show how often disease occurs, which helps researchers identify disease trends and track disease outbreaks. U.S. Navy has a modified list of reportable medical events to accommodate for deployment limiting functions. Reports on all reportable events are submitted to the Naval Disease Reporting System (NDRS). Medical event surveillance is particularly important in the military populations where medical events can have mission-degrading implications and affect troop strength.

Objective

The purpose of the study was to determine whether, through the use of existing electronic laboratory and clinical care databases, it is possible to capture the majority of reportable disease cases, and remove the burden of case finding from the commands through NDRS. Establishment of a more efficient reporting system was proposed to provide more timely disease reporting and aid in active disease surveillance.

Objective

To demonstrate how natural language processing (NLP) of clinical records can contribute to case detection and characterization in biosurveillance.

Syndromic surveillance has traditionally been used by public health in disease epidemiology. Partnerships between hospital-based and public health systems can improve efforts to monitor for disease clusters. Greenville Hospital System operates a syndromic surveillance system, which uses EARS-X to monitor chief complaint, lab, and radiological data for the four emergency departments within the hospital system. Combined, the emergency departments have approximately 145,000 visits per year. During March 2007 an increase in invasive group A Streptococcus (GAS) disease in the community lead to the use of syndromic surveillance to determine if there was a concomitant increase in Scarlet Fever within the community.

Objective

 Demonstrate the utility of collaboration between hospital-based and public health syndromic surveillance systems in disease investigation. Demonstrate the ability of syndromic surveillance in identification and evaluation of process improvements.

Time series analysis is very popular in syndromic surveillance. Mostly, public health officials track in the order of hundreds of disease models or univariate time series daily looking for signals of disease outbreaks. These time series can be aggregated counts of various syndromes, possibly different genders and age-groups. Recently, spatial scan algorithms find anomalous regions by aggregating zipcode level counts [1]. Usually, public health officials have a set of disease models (for e.g. fever or headache symptom in male adults is indicative of a particular disease). Based on the past experience public health officials track these disease models daily to find anomalies that might be indicative of disease outbreaks. A typical syndromic surveillance system these days will track in the order of 100-200 time series on daily basis using different univariate algorithms like CUSUM, moving average, EWMA, etc.

Let us consider a representative dataset of a state which has 100 zipcodes that monitors 10 syndromes among 3 age groups and 2 genders in emergency rooms. There are a total of 6,000 (100 x 10 x 3 x 2) distinct time series for a particular zipcode, syndrome, age-group and gender. This number already seems too high to monitor daily. Hence most syndromic systems only monitor state level aggregates for all syndromes or a few combinations of syndromes, gender and age-groups.

But most real world disease models are more complex and affect multiple syndromes, or multiple agegroups. We need to analyze more complex streams that aggregate multiple values in the attributes to mine more interesting patterns not seen otherwise. As an example, a massive search could reveal that recently senior female patients having fever and nausea have increased in the north eastern part of the state.

Objective

This paper shows how T-Cubes, a data structure that makes tracking millions of disease models simultaneously feasible, can be used to perform multivariate time series analysis using primitive univariate algorithms. Hence, the use of T-Cube in brute-force search helps identify stronger disease outbreak signals currently missed by the surveillance systems.

Clinicians can pursue the clinical findings for specific patients until reaching a diagnosis in real time.  When using electronic ED complaints, one relies on symptoms volunteered by patients in the triage setting.  Patients seek emergency care at different stages of disease and there is scant information detailing how they respond when allowed only 2-3 complaints.  Our emergency department (ED) clinical data warehouse includes date, demographics, complaints, diagnosis, laboratory results, and disposition. We used a process similar to reverse engineering to augment our ability to detect chief complaints and test results consistent with MEE.  We started with the diagnosis of MEE and examined the chief complaints and diagnostic findings in patients diagnosed with MEE to develop expanded algorithms.

Objective

Our research questions were:

1.) could we use existing data to empirically improve our syndrome surveillance algorithms?

2.) Is it feasible to combine disparate data sources to detect the same event? We studied these questions using the meningoencephali-tis (MEE) syndrome and the West Nile Virus Chicago outbreak in 2002.

Free text chief complaints (CCs), which may be recorded in different languages, are an important data source for syndromic surveillance systems. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. However, CCs in different languages pose technical challenges for the development of multilingual CC classifiers.  We addressed the technical challenges by first developing a ontology-enhanced CC classifier which exploits semantic relations in the Unified Medical Language System (UMLS) to expand the knowledge of a rule-based CC classifier. Based on the ontologyenhanced English CC classifier, a translation module was incorporated to extract symptom-related information in Chinese CCs and translate it into English. This design thus enables the processing of CCs in both English and Chinese. 

Objective  

This paper describes the effort to design and implement a chief complaint (CC) classification system that is capable of processing CCs in both English and Chinese.

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.

Temporal anomaly detection is a key component of real time surveillance. Today, despite the abundance of temporal information on multiple syndromes, multivariate investigation of temporal anomalies remains under-explored. Traditionally, an outbreak is thought of as disease localization in time. That is, for an event to qualify as an outbreak, a significant deviation from the observed distribution of the disease must occur.  However, the underlying processes that govern the health seeking behavior of a population with respect to one disease can potentially impact multiple syndromes leading to observable correlation patterns in the daily rates of those syndromes. Thus, a deviation from the observed correlation pattern between different syndromes can be an early indicator of potential anomalies when the rise in the daily rates of one or more syndrome is not sufficiently discernable to be identified by standard univariate techniques.

Objective

The objectives of this study are to develop a mathematical multi-syndrome framework for early detection of temporal anomalies, to demonstrate improvement in detection sensitivity and timeliness of the multivariate technique compared with those of standard uni-syndrome analysis, and to put forward a new practical concept for timely outbreak investigation.

In September 2004, Kingston, Frontenac and Lennox and Addington Public Health began a 2-year pilot project to develop and evaluate an Emergency Department Chief Complaint Syndromic Surveillance System in collaboration with the Ontario Ministry of Health and Long Term Care – Public Health Branch, Queen’s University, Public Health Agency of Canada, Kingston General Hospital and Hotel Dieu Hospital. At this time, the University of Pittsburgh’s Real-time Outbreak and Disease Surveillance (RODS, Version 3.0) was chosen as the surveillance tool best suited for the project and modifications were made to meet Canadian syndromic surveillance requirements. To evaluate the design and implementation of the system, a multi-sectored approach to evaluation was taken. Individual evaluations of the process, technical aspects and of cost/benefit were conducted to demonstrate proof of concept and the associated costs. An overall outcome or effectiveness evaluation will take place in spring 2006.

Objective

This paper outlines the approach used to evaluate an emergency department syndromic surveillance system on the following areas: process and outcome, cost/benefit and technical.

Existing statistical methods can perform well in detecting simulated bioterrorism events. However, these methods have not been well-evaluated for detection of the type of respiratory and gastrointestinal events of greatest interest for routine public health practice. To assess whether a syndromic surveillance system can detect these outbreaks, we constructed simulated outbreaks based on public health interest and experience. We then inserted these outbreaks into real data. We assessed whether the simulated outbreaks could be detected using a battery of detection methods, including model-adjusted scan statistics and space-time permutation scan statistics.

Objective

We used simulation methods to assess the performance of two distinct anomaly-detection approaches, each under a variety of parameter settings, with respect to their ability to detect outbreaks of commonly occurring events of public health importance.