Pilot Study

Syndromic surveillance data such as the incidence of influenza-like illness (ILI) is broadly monitored to provide awareness of respiratory disease epidemiology. Diverse algorithms have been employed to find geospatial trends in surveillance data, however, these methods often do not point to a route of transmission. We seek to use correlations between regions in time series data to identify patterns that point to transmission trends and routes. Toward this aim, we employ network analysis to summarize the correlation structure between regions, whereas also providing an interpretation based on infectious disease transmission. 

Cross-correlation has been used to quantify associations between climate variables and disease transmission. The related method of autocorrelation has been widely used to identify patterns in time series surveillance data. This research seeks to improve interpretation of time series data and shed light on the spatial–temporal transmission of respiratory infections based on cross-correlation of ILI case rates.

Objective

Time series of ILI events are often used to depict case rates in different regions. We explore the suitability of network visualization to highlight geographic patterns in this data on the basis of cross-correlation of the time series data. 

Electronic disease surveillance systems can be extremely valuable tools; however, a critical step in system implementation is collection of data. Without accurate and complete data, statistical anomalies that are detected hold little meaning. Many people who have established successful surveillance systems acknowledge the initial data collection process to be one of the most challenging aspects of system implementation. These challenges manifest from varying degrees of economical, infrastructural, environmental, cultural, and political factors. Although some factors are not controllable, selecting a suitable collection framework can mitigate many of these obstacles. JHU/APL, with support from the Armed Forces Health Surveillance Center, has developed a suite of tools, Suite for Automated Global bioSurveillance, that is adaptable for a particular deployment’s environment and takes the above factors into account. These subsystems span communication systems such as telephone lines, mobile devices, internet applications, and desktop solutions - each has compelling advantages and disadvantages depending on the environment in which they are deployed. When these subsystems are appropriately configured and implemented, the data are collected with more accuracy and timeliness.

Objective

This paper describes the common challenges of data collection and presents a variety of adaptable frameworks that succeed in overcoming obstacles in applications of public health and electronic disease surveillance systems and/or processes, particularly in resource-limited settings.

Given the periodic nature of influenza activity, it is important to develop visualization tools that enable enhanced decision-making. User-Centered Design is a set of software development methodologies that primarily employ user needs to develop applications. Similarly, Usability Heuristics provide a set of rules that increase the performance of user interfaces, and ease of use. We combined some of these techniques to develop FluView Interactive, a prototype that will enable users to better understand influenza information.

Objective

The objective of this study is to report on the use of User-Centered Design and Usability Heuristics to improve visualization of influenza-related information at the national level. The intention of the prototype is to make data more accessible to different stakeholders including the general public, public health officials at the local and state level, and other experts.

Mandatory notification to public health of priority communicable diseases (CDs) is a cornerstone of disease prevention and control programs. Increasingly, the addresses of CD cases are used for spatial monitoring and cluster detection and public health may direct interventions based on the results of routine spatial surveillance. There has been little assessment of the quality of addresses in surveillance data and the impact of address errors on public health practice.

We launched a pilot study at the Montreal Public Health Department, wherein our objective was to determine the prevalence of address errors in the CD surveillance data. We identified address errors in 25% of all reported cases of communicable diseases from 1995 to 2008. We also demonstrated that address errors could bias routine public health analyses by inappropriately flagging regions as having a high or low disease incidence, with the potential of triggering misguided outbreak investigations or interventions. The final step in our analysis was to determine the impact of address errors on the spatial associations of campylobacter cases in a simulated point source outbreak.

Objective

To examine, via a simulation study, the potential impact of residential address errors on the identification of a point source outbreak of campylobacter.

More than a decade ago, in collaboration with the U.S. Department of Defense, the Johns Hopkins University Applied Physics Laboratory (JHU/APL) developed the Electronic Surveillance System for the Early Notification of Community-based Epidemics (Enterprise ESSENCE), which is currently used by federal, state and local health authorities in the US. As emerging infections will most likely originate outside of the US (for example, SARS) the application of electronic biosurveillance is increasingly important in resource limited areas. In addition, such systems help governments respond to the recently modified International Health Regulations. Leveraging the experience gained in the development of Enterprise ESSENCE, JHU/APL has developed two freely available electronic biosurveillance systems suitable for use in resource-limited areas: Open ESSENCE (OE) and ESSENCE Desktop Edition (EDE).

Objective

This paper describes the development and early implementation of two freely available electronic biosurveillance software applications: OE, and EDE.

Public health departments have a strong interest in monitoring the incidence, care, and complications of gestational diabetes, as it is associated with poor outcomes for infants and increased risk of diabetes type II for mothers. Gestational diabetes rates are also a possible early marker for changes in the incidence of diabetes type II in the general population. However, diabetes is not generally a reportable condition and therefore, public health surveillance is limited to periodic telephone surveys (subject to self-report inaccuracies), sponsored clinical examinations (expensive, small sample size, no information about processes of care), and occasional research studies. Automated analysis of electronic health record data is a promising method to complement existing surveillance tools with longitudinal, continually updated, clinically rich data derived from large populations. We describe a pilot project to automatically survey electronic health record data in order to identify cases of gestational diabetes, describe their patterns of care and complications, and report summary data to the state health department.

Objective

To develop an electronic, prospective surveillance system to describe the incidence, care, and complications of gestational diabetes using live electronic health record data from a large defined population.

The Distribute project began in 2006 as a distributed, syndromic surveillance demonstration project that networked state and local health departments to share aggregate emergency department-based influenza-like illness (ILI) syndrome data. Preliminary work found that local systems often applied syndrome definitions specific to their regions; these definitions were sometimes trusted and understood better than standardized ones because they allowed for regional variations in idiom and coding and were tailored by departments for their own surveillance needs. Originally, sites were asked to send whatever syndrome definition they had found most useful for monitoring ILI. Places using multiple definitions were asked to send their broader, higher count syndrome. In 2008, sites were asked to send both a broad syndrome, and a narrow syndrome specific to ILI.

Objective

To describe the initial phase of the ISDS Distribute project ILI syndrome standardization pilot.