ISDS Conference

Computational and statistical methods for detecting disease clusters, such as the spatial scan statistic, have become frequently used tools in epidemiology. However, they simply tell the user where a cluster is, and leave the analysis task to the user. Multivariate visualization tools provide one way for this analysis. The approach developed in this research is computational in nature, using computer vision techniques to analyze the shape of the cluster. Shapes are used here because different spatial processes that cause clusters, such as pollution along a river, create clusters with different shapes. Thus, it may be possible to categorize clusters by their respective spatial processes by analyzing the cluster shapes.

OBJECTIVE

There are plenty of computational and statistical methods for detecting spatial clusters, although the interpretation of these clusters is a task left to the user. This research develops computational methods to not just detect, but also analyze the cluster to hypothesize one or more potential causes.

Approximately one quarter of people treated for tuberculosis (TB) have no supporting microbiology, and thus are not detectable through laboratory reporting systems. Health departments depend upon clinicians to report these cases, but there is important underreporting. We previously described the performance of several algorithms for TB detection using electronic medical record (EMR) and claims data, and noted good sensitivity when screening for >2 anti-TB drugs; however, the positive predictive value was only 30%. We re-evaluated this and other algorithms in light of evolving TB treatment practices and enhanced ability to apply complex decision rules to EMR data in real time.

Objective

To develop algorithms for case detection of TB using EMR data to improve notifiable disease reporting.

Objective

We performed a gold-standard manual chart review for gastro-intestinal syndrome to evaluate automated detection models based on both structured and non-structured data extracted from the VA electronic medical record.

The ability to provide real time syndromic surveillance throughout the Capital Health Region is currently undeveloped. There are limited mechanisms for routine real time surveillance of disease or conditions of public health interest, e.g. communicable diseases, toxic exposure or injury. Toxic exposure and injury while preventable are not notifiable in Alberta and as a consequence there is no real-time surveillance system to identify burden of disease or opportunities for intervention. The notifiable disease system is reliant on paper-based forms which are slow, prone to human error, and labor intensive to convert to electronic database format for flexible analysis and interpretation. Finally there is no system to link the data collected on the same individual in each database without compromising confidentiality. ARTSSN is designed to remedy these deficiencies.

Objective

In this presentation we describe the creation of an IT architecture and infrastructure to integrate data from four sources to support real-time syndromic surveillance for injuries, toxic exposures and notifiable diseases in Capital Health, Alberta, Canada.

New York City ED syndromic surveillance data uses SaTScan to detect spatial signals. SaTScan analysis has been integrated into SAS since 2002, and signal maps have been generated from SAS since 2003. Signal maps are created occasionally to investigate a severe outbreak based on the SaTScan results. Previous use and integration of additional GIS analysis in ArcGIS has been done manually, requiring more time, and running the risk of being less consistent than an automated method. This script now integrates the SAS, SaTScan and spatial analysis from ArcGIS to create high-quality maps in an automated procedure.

Objective

The objective was to minimize the amount of time spent on routine, daily analysis of syndromic data, integrate additional spatial analysis, create better maps, and cut response times to outbreaks.

Recent extreme weather events have caused serious health and social problems across Europe. During the summer heat waves of 2003 across Europe, France recorded an excess of over 14,000 deaths contributed to heat-related causes. Other countries such as Italy and Portugal experienced over 3,000 and over 2,000 excess deaths respectively. The extreme rises in mortality were initially unobserved by traditional public health surveillance techniques; morbidity related to heat-related exposures also went initially unnoticed by public health authorities.

Real-time monitoring of clinical data has been proposed as one method of surveillance that may be used to alert public health authorities during extreme weather conditions when heat-related morbidity may be higher than expected. Previous studies have shown increased ambulance calls during heat alert conditions in Canada. These potential data sources, including electronic medical records for emergency department visits, are already in existence in many of the countires affected by the heat waves of 2003. Syndromic surveillance methods such as those described by Mandl et al could be applied to these data to help detect when heat-related morbidity and possibly heat-related mortality begins to rise.

Objective

The specific objectives of the study are to evaluate the usefulness of syndromic surveillance data to monitor heat-related morbidity and mortality during extreme weather conditions. During such conditions, real time data monitoring could potentially help drive interventions to reduce morbidity and mortality.

We describe the development and implementation of a protocol for identifying syndromic signals and for assessing their value to public health departments for routine (non-bioterrorism) purposes. The specific objectives of the evaluation are to determine the predictive value positive, sensitivity, and timeliness of the surveillance system, as well as its costs and benefits to public health.

We describe here a multilingual ontology to support disease surveillance by intelligent text mining systems from Web-based rumours. We informally assessed the coverage of its English terms on large sample of news collected from the Web.

To assess the accuracy of community-based physician claims for identifying 5 syndromes: fever, gastrointestinal, neurological, rash, and respiratory.

1) Describe a near real-time school-based syndromic surveillance program that integrates electronic data records and a two-way health alert system for early outbreak detection, notification, and possible intervention for Arizona schools. 2) Demonstrate the public health utility of this system for early detection of influenza among school children.