ISDS Conference

When the Chicago Bears met the Indianapolis Colts for Super Bowl XLI in Miami in January, 2007, fans from multiple regions visited South Florida for the game. In the past, public health departments have instituted heightened local surveillance during mass gatherings due to concerns about increased risk of disease outbreaks. For the first time, in 2007, health departments in all three Super Bowl-related regions already practiced daily disease surveillance using biosurveillance information systems (separate installations of the ESSENCE system, developed at JHUAPL). The situation provided an opportunity to explore ways in which separate surveillance systems could be coordinated for effective, short-term, multijurisdictional surveillance.

Objective

This paper describes an inter-jurisdictional surveillance data sharing effort carried out by public health departments in Miami, Chicago, and Indianapolis in conjunction with Super Bowl XLI.

The 2005 Youth Risk Behavior Survey of 9th to 12th graders in Miami-Dade County public schools found that 69.7% of students tried alcohol, 28.3% tried marijuana, and 6.3% tried cocaine in their lifetime. Results also showed that Hispanics had a higher percentage of usage when compared to Blacks or Whites. The 2007 White House Office of National Drug Control Policy special report entitled “Hispanic Teens and Drugs” also concluded that Hispanics were at the highest risk for substance abuse. With the county’s 60% Hispanic population, this issue is of concern for the community. This is the first study to compare multiple sources of data to describe substance abuse among youth from areas such as healthcare utilization to criminal charges.

Identifying potential biases and confounders that may affect data quality is an important consideration when evaluating surveillance systems. Having the benefit of predictable temporal trends is a key requirement to improve upon the specificity of detecting outbreaks. Identification of factors that impact on the reliability of the temporal trends observed in the data may provide for the ability to improve the capability to identify aberrations in those trends. During a retrospective study of a dataset of microbiology orders from the veterinary teaching hospital at The Ohio State University for 2003 we noticed regular intervals when increases in the number of culture orders were not accompanied by proportional increases in the number of isolates. These instances appeared to occur at intervals that coincided with the clinical rotation of senior veterinary students within the hospital.

Objective

This paper reports on a potential confounder discovered during an investigation of microbiology orders in a veterinary teaching hospital as a possible data source for outbreak detection.

The practice of real-time disease surveillance, sometimes called syndromic surveillance, is widespread at local, state, and national levels. Diseases ignore legal boundaries, so situations frequently arise where it is important to share surveillance information between public health jurisdictions. There are currently two fundamental ways for systems to share public health data and information related to disease outbreaks: sharing data, or sharing information. Data refers to patient level and aggregate counts of patients, and can be difficult to share legally because of privacy issues. Information refers to summaries, opinions or conclusions about data. There are few if any legal barriers to sharing information, and by definition it includes interpretation of data by knowledgeable local personnel which is vital during outbreak investigation. Currently most shared information is unstructured text, and this format makes it difficult for computers to use the information in any meaningful way. The only thing a system can do with this unstructured information is allow users to read each message.

Objectives

Alternate methods are needed to facilitate communication between jurisdictions during potential disease outbreaks. One alternative is to share structured information. Defined at the appropriate level, information sharing can avoid traditional data sharing barriers while capturing valuable local knowledge. The key is to identify the types of surveillance information that are neither so highly interpreted as to lose their value nor so loosely interpreted as to face traditional data sharing barriers. The objective of this work is to identify the level at which surveillance information sharing can be both feasible and beneficial, and to create a vocabulary standard that supports the exchange of structured information between diverse surveillance systems. 

Numerous methods have been applied to the problem of modeling temporal properties of disease surveillance data; the ESSENCE system contains a widely used approach (1). STL (2) is a flexible, wellproven method for temporal modeling that decomposes the series into frequency components. A periodic component like DW can be exactly periodic or evolve through time. STL is based on loess (3), which can model a numeric response as a function of any explanatory variables. After the STL modeling of the counts, we will add patient address and produce a timespace modeling using both STL and more general loess methods.

Objective

Use the STL local-regression (loess) decomposition procedure and transformation to model the univariate time-series characteristics of chief-complaint daily counts as a first step in a time and spatial modeling. Develop visualization tools for model display and checking.

Bordetella Pertussis outbreaks cause morbidity in all age groups, but the infection is most dangerous for young infants. Pertussis is difficult to diagnose, especially in its early stages, and definitive test results are not available for several days. Because of temporal and geographic variability of pertussis outbreaks, delay in diagnostic test results and ramifications of incorrect management decisions at the point of care, pertussis represents a prototypical disease where realtime public health surveillance data might inform, guide and improve medical decision making. Previously, we showed that diagnostic accuracy for meningitis can be improved when information about recent, local disease incidence is accounted for. Here, we quantify the contribution of epidemiologic context to a clinical prediction model for pertussis using a state public health data stream.

Objective

To explore the integration of epidemiological context – current population-level disease incidence data – into a clinical prediction model for pertussis.

Washoe County District Health Department (WCDHD) is a local health district serving nearly 400,000 residents in Washoe County including cities of Reno and Sparks, the second largest urban area in Nevada. To enhance overall public health surveillance capacities in the agency, WCDHD officially implemented National Retail Data Monitor (NRDM) in September 2004, Real-time Outbreak & Disease Surveillance (RODS) in July 2005, and FirstWatch in August 2005. These three systems monitor over-the-counter sales for medications and healthcare products, chief complaints at emergency room visits, and 911 calls, respectively. Preliminary evaluation of NRDM suggested the usefulness of system. The addition of RODS and FirstWatch also demonstrated the utility in assisting outbreak investigation during the past few months. Unfortunately no written protocols are in place to guide program staff to manage alerts in a standardized fashion and make appropriate responses. Such guidelines from federal or state level are not yet available as we are aware, however, such protocol is highly needed.

Objective

The objective of this paper is to describe the standard operation procedures for three existing syndromic surveillance systems in WCDHD, Nevada.

For more than a decade, biosurveillance systems (and more recently BioSense) have been employed in the United States. Efforts to drastically expand these surveillance capacities have been a national priority given concerns about national security. However, there has been little emphasis on value or increasing value to communities or agencies contributing and analyzing data. This qualitative analysis focused on all biosurveillance stakeholders and the opportunity to enhance interoperability and reuse of data and systems.

Objective

To understand the perspective of biosurveillance stakeholders and how their participation creates value for them as well as public health departments.

As the Georgia Division of Public Health began constructing a systems interface for its syndromic surveillance program, the nature and intended use of these data inspired new approaches to interface design. With the temporal and spatial components of these data serving as fundamental determinants within common aberration detection methods (e.g., Early Aberration Reporting System, SaTScan™), it became apparent that an interface technique that could present a synthesis of the two might better facilitate the visualization, interpretation and analysis of these data.

Typical presentations of data spatially oriented at the zip code level use a color gradient applied to a zip code polygon to represent the differences in magnitude of events within a given region across a particular time span. Typical presentations of temporally oriented data use time series graphs and tabular formats. Visualizations that present both aspects of spatially and temporally rich datasets within a single visualization are noticeably absent.

Objective

This paper describes an approach to the visualization of disease surveillance data through the use of animation techniques applied to datasets with both temporal and geospatial components.

Tuberculosis (TB) has reemerged as a global public health epidemic in recent years. TB remains a serious public health problem among certain patient populations, and is prevalent in many urban areas. The World Health Organization estimates that approximately nine million individuals will develop active TB disease and more than two million will die from TB. The global burden of TB remains enormous, and will likely rank high among public health problems in the coming decades. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. In this study, we used the space-time scan statistic to identify where and when the prevalence of TB is high in Fukuoka Prefecture. The ability to detect disease outbreaks is important for local and national health departments to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Because the statistic meets these needs completely, results that are effective and practical for public health officials are expected from this study.