Surveillance

The VA has employed ESSENCE for health monitoring since 2006 [1]. Epidemiologists at the Office of Public Health (OPH) monitor the VA population at the national level. The system is also intended for facility-level monitoring to cover 152 medical centers, nearly 800 community-based outpatient clinics (CBOC), and other facilities serving all fifty states, the District of Columbia, and U.S. territories. For the entire set of facilities and current syndrome groupings, investigation of the full set of algorithmic alerts is impractical for the group of monitors using ESSENCE. Signals of interest may be masked by the nationwide alert burden. Customized querying features have been added to ESSENCE, but standardization and IP training are required to assure appropriate use.

Objective

The objective was to adapt and tailor the alerting methodology employed in the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE) used by Veterans Affairs (VA) for routine, efficient health surveillance by a small, VA headquarter medical epidemiology staff in addition to a nationwide group of infection preventionists (IPs) monitoring single facilities or facility groups.

Hypoglycemia is a serious sequela of diabetes treatment that is not tracked by current health surveillance efforts despite substantial related morbidity and mortality. We take a novel approach to hypoglycemia surveillance, engaging members of an international online diabetes social network in reporting about this issue as members of a consented, distributed public health research cohort.

Objective

To measure the prevalence of hypoglycemic episodes and associated harms among participants in an international, online diabetes social network.

Recent events have focused on the role of emerging and re-emerging diseases not only as a significant public health threat but also as a serious threat to the economy and security of nations. The lead time to detect and contain a novel emerging disease or events with public health importance has become much shorter, making developing countries particularly vulnerable to both natural and man-made threats. There is a need to develop disease surveillance systems flexible enough to adapt to the local existing infrastructure of developing countries but which will still be able to provide valid alerts and early detection of significant public health threats.

Objective

To determine system usefulness of the ESSENCE Desktop Edition in detecting increases in the number of dengue cases in the Philippines.

The Veterans Health Administration (VHA) is the VA organization responsible for providing healthcare to over 5 million patients annually at 153 medical centers and over 900 outpatient clinics across the United States and U.S. territories. The VA Subject Matter Expertise Center for Biological Events (SMEC-bio) aims to leverage data in the extensive VHA electronic health records system and other sources to provide decision support to leadership for emerging infectious disease threats. Initial SMEC-bio work to examine this capability suggested that the increased incidence of dengue disease in the VHA patient population in PR in 2010 may be related to increased rainfall (see reference). This present work analyzes dengue incidence in the PR VHA patient population over time to understand disease trends and contribute to a framework for predictive analysis. This paper describes trend analyses of dengue and dengue-like illness in VHA patient data in Puerto Rico (PR) with the goal of developing mechanisms for improved early warning and situational awareness of infectious disease threats.

Cross-jurisdictional sharing of public health syndrome data is useful for many reasons, among them to provide a larger regional or national view of activity and to determine if unusual activity observed in one jurisdiction is atypical. Considerable barriers to sharing of public health data exist, including maintaining control of potentially sensitive data and having informatics systems available to take and view data. The Distribute project [1,2] has successfully enabled cross-jurisdictional sharing of ILI syndrome data through a community of practice approach to facilitate control and trust, and a distributed informatics solution. The Gossamer system [3] incorporates methods used in several UW projects including Distribute. Gossamer has been designed in a modular fashion to be hosted using virtual or physical machines, including inside cloud environments. Two modules of the Gossamer system are designed for aggregate data sharing, and provide a subset of the Distribute functionality. The Distribute and Gossamer systems have been used for ad-hoc sharing in three different contexts; sharing of common ILI data for research into syndrome standardization, sharing syndromic data for specific events (2010 Olympics) and for pilot regional sharing of respiratory lab results. Two additional projects are underway to share specific syndromes of recent interest: alcohol related and heat related ED visits.

Objective

To demonstrate how rapid adhoc sharing of surveillance data can be achieved through informatics methods developed for the Distribute project.

Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute project provides graphic comparisons of both ILI-related clinical visits across jurisdictions and a national picture of ILI. Unlike other surveillance systems, Distribute is designed to work solely with summarized (aggregated) data which cannot be traced back to the un-aggregated 'raw' data. This and the distributed, voluntary nature of the project creates some unique data quality issues, with considerable site to site variability. Together with the ISDS, the University of Washington has developed processes and tools to address these challenges, mirroring work done by others in the Distribute community.

Objective

To present exploratory tools and methods developed as part of the data quality monitoring of Distribute data, and discuss these tools and their applications with other participants.

Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance (ISDS) for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance (ISDS) for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute project provides graphic comparisons of both ILI-related clinical visits across jurisdictions and a national picture of ILI. Unlike other surveillance systems, Distribute is designed to work solely with summarized (aggregated) data which cannot be traced back to the un-aggregated 'raw' data. This and the distributed, voluntary nature of the project create some unique data quality issues, with considerable site to site variability. Together with the ISDS, the University of Washington has developed processes and tools to address these challenges, mirroring work done by others in the Distribute community.

Objective

The goal of this session will be to briefly present two methods for comparing aggregate data quality and invite continued discussion on data quality from other surveillance practitioners, and to present the range of data quality results across participating Distribute sites.

Chronic diseases are the leading causes of mortality and morbidity for Americans but public health surveillance for these conditions is limited. Health departments currently use telephone interviews, medical surveys, and death certificates to gather information on chronic diseases but these sources are limited by cost, timeliness, limited clinical detail, and/or poor population coverage. Continual and automated extraction, analysis, and summarization of EHR data could advance surveillance in each of these domains.

Objective

Develop methods for automated chronic disease surveillance and visualization using electronic health record (EHR) data.

Animal bites may have potentially devastating consequences, including physical and emotional trauma, infection, rabies exposure, hospitalization, and, rarely, death. NC law requires animal bites be reported to local health directors. However, methods for recording and storing bite data vary among municipalities. NC does not have a statewide system for reporting and surveillance of animal bites. Additionally, many animal bites are likely not reported to the appropriate agencies. NC DETECT provides near-real-time statewide surveillance capacity to local, regional, and state level users with twice daily data feeds from NC EDs. Between 2008 and 2010, 110 to 113 EDs were submitting visit data to NC DETECT. Several animal bite-related on-line reports are available and provide aggregate and visit-level analyses customized to users' respective jurisdictions. The NC DETECT ED visit database currently provides the most comprehensive and cost-effective source of animal bite data in NC.

Objective

We describe the use of emergency department (ED) visit data collected through the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) for surveillance of animal bites in North Carolina (NC). Animal bite surveillance using ED visit data provides useful and timely information for public health practitioners involved in bite surveillance and prevention in NC.

Adverse drug events (ADEs) are a major cause of morbidity and mortality. However, post-marketing surveillance systems are passive and reporting is generally not mandated. Thus, many ADEs go unreported, and it is difficult to estimate and/or anticipate side effects that are unknown at the time of approval. ADEs that are reported to the FDA tend to be severe, and potentially common, but less serious side effects are more difficult to characterize and document. Drugs with a high risk of harm outweighing the therapeutic value have recently been subjected to a greater level of interest with the Food and Drug Administration's Risk Evaluation and Mitigation Strategies (REMS). However, no rapid method to detect if the REMS produce the desired effect and assessment of the impact is conducted by the drug manufacturer. Increasingly, Americans have been turning to the internet for health related information, largely by the use of search engines such as Google. The volume of searches for drugs and ADEs provides a unique insight about the interest in various medications and side effects as well as longitudinal changes.

Objective

To investigate the use of search volume data from Google Insight for the detection and characterization of adverse drug events.