ISDS Conference

ESSENCE receives and analyzes data for the Military Health System’s (MHS) 9 million beneficiaries resulting in approximately 90,000 daily outpatient and emergency department visits worldwide. In May 2008, MHS released ESSENCE Version 2.0, a system-wide upgrade which includes the following enhancements: improved system security, additional reporting and display capabilities, laboratory orders, radiology orders, and the ability for users to define their own syndrome groups.

Objective

As an evolving syndromic surveillance system, ESSENCE has recently undergone some significant improvements and new additional capabilities. We present three of these impactful enhancements and evaluate their added value to military public health and preventive medicine providers and system users. Specific Version 2.0 enhancements include: (1) laboratory orders (2) radiology orders and (3) the ability for users to create their own syndrome groups for outbreak classification and detection.

In the last decade, time series analysis has become one of the most important tools of surveillance systems. Understanding the nature of temporal fluctuations is essential for successful development of outbreak detection algorithms, aberration assessment, and to control for seasonal variations. Typically, in applying the time series methods to health outcomes collected over an extended period of time it is assumed that population profiles remain constant. In practice, such assumptions have been rarely tested. At best, the temporal analysis is performed using stratification by age or other discriminating factors if heterogeneity is suspected. Any community can experience population changes in various forms. Long-term trends of inflow/outflow migration and rapid transient fluctuations associated with specific events are typical examples of changes in population profile. Seasonality, as an intrinsic property of infectious diseases manifestation in a community, is typically attributed to periodic changes in transmissibility of pathogens. To some extent, seasonal fluctuations in the incidence of infectious diseases could also be associated with the changes in population profiles. The ability to detect and describe such changes would provide valuable clues into seasonally changing factors associated with an infection.

Objective

The objective of this communication is two-fold: 1) to introduce an analytical approach for assessing temporal changes in the surveillance reporting with respect to population profile; and 2) to demonstrate the utility of this method using laboratory-confirmed cases for four reportable enteric infections (cryptosporidiosis, giardiasis, shigellosis, and salmonellosis) recorded by the Massachusetts Department of Public Health over the last 12 years. This new approach for assessing seasonal changes is based on comparison of gender-specific single-year age distributions, which constitute population profiles.

In the fall of 2006, the Ohio Department of Health (ODH) and the Indiana State Department of Health (ISDH) proactively began general discussions regarding surveillance issues of mutual interest. Both states, having operational syndromic surveillance systems, thought value could be added to one another’s program by sharing data across their common border. Ohio receives emergency department chief complaint data from 130 of its hospitals; Indiana from 76 hospitals. The ODH uses the EpiCenter System managed by Health Monitoring Systems, while the ISDH Public Health Emergency Service System uses Electronic Surveillance System for the Early Notification of Communitybased Epidemics. Each state desired to view the new shared data through its own system. A formal memorandum of understanding was developed and signed by both states to support syndromic data sharing. Data began flowing between the two states in April, 2008.

Objective

The ODH and the ISDH enhanced their individual syndromic surveillance efforts through cross-border sharing of emergency department chief complaint data.

Capital Health is a regional health care organization, which provides services for over one million inhabitants in the Edmonton area of Alberta, Canada. Traditionally, disease surveillance under its jurisdiction has been paper-based and records maintained by different departments in several locations. Before the Alberta Real Time Syndromic Surveillance Net (ARTSSN), there was no centralized database or unified approach to surveillance and automated reporting despite rich electronic health data in the region. The existing labor-intensive manual surveillance process is inefficient and inherently susceptible to human error. Its effectiveness is sub-optimal in detecting outbreaks of emerging infectious diseases, and clusters of injuries or toxic exposures. The ultimate objective of ARTSSN is to enhance public health surveillance through earlier and more sensitive detection of clusters and trends, with subsequent tracking and response through an integrated, automated surveillance and reporting system.

Objective

ARTSSN is a pilot public health surveillance project developed for the Capital Health region of Alberta, Canada and funded by Alberta Health and Wellness. This paper describes the advantages of using ARTSSN and comparing information derived from multiple electronic data sources simultaneously for real time syndromic surveillance.

Regional disease surveillance systems allow users the ability to view large amounts of population health information and examine automated alerts that suggest increased disease activity. These systems require users to view and interpret which of these alerts or data streams are epidemiologically important. This interpretation is valuable information that may benefit other users. In addition to the daily interpretation of data done by users, the ability to communicate local concerns and findings during a public health event to neighboring jurisdictions is of great public health importance. Public health officials also need constant situational awareness and a venue to share their concerns about increases in disease activity before a health emergency is declared. The Event Communications Component (ECC) was created to provide this venue. The ECC was developed for the National Capital Region (NCR) public health surveillance network to facilitate the need for users to communicate. The NCR system is an operational multi-jurisdictional biosurveillance system employed in the District of Columbia and in surrounding Maryland and Virginia counties. NCR users include epidemiologists and public health officials from different levels of government. The ECC has been in operation for a year in the NCR system. ECC 2.0 is being developed to improve on the original version’s capabilities and solve its shortcomings.

 

Objective

Identify areas of improvement and establish design goals of ECC 2.0. These design goals include: the incorporation of comment centric design versus event centric, automatic notification of new events/comments, the use of action oriented concern levels and user interface improvements. Focus design goals by utilizing prototyping and user group reviews. Develop ECC 2.0 and integrate it into the NCR system.

The Centre for Health Protection in Hong Kong has operated a sentinel surveillance system for infectious diseases at child care centre (CCC) since March 2004, among its multi-faceted disease surveillance systems. Forty-six CCCs have participated in the system and are contributing data weekly on absenteeism and common infectious disease symptoms such as fever, diarrhea, vomiting, and cough. The system was originally driven by a manual data collection mechanism via fax, followed by secondary data input and subsequent analysis. However, such mechanism might sometimes result in delayed data transmission and data loss. As an alternative to accommodate these limitations, a web-based platform is developed to increase the timeliness of data submission by the sentinel CCCs. The new platform not only speeds up data collection and eliminates the need for human data entry, but at the same time delivers summary statistics directly on the web through computer programmes on a real time basis, as soon as data is entered by the provider.

Objective

This paper describes the attempt to develop an internet-based community surveillance network to enhance timeliness and sensitivity in detecting community-wide infectious disease outbreaks among young children at CCCs in Hong Kong.

A large event such as the Super Bowl that attracts over 120,000 visitors to an area within a short period of time has the potential to increase the risk of communicable diseases and environmental hazards in a community in addition to the possibility of a bioterrorist attack. Though Miami-Dade County Health Department has in place a syndromic surveillance system, additional public health measures were implemented to ensure the health and safety of all residents and visitors in the weeks surrounding the February 4th event.

OBJECTIVE

To identify unusual patterns of communicable diseases, health events or bioterrorism-related activity in Miami-Dade County immediately before, during and after Super Bowl XLI.

Public health officials are now receiving more data than ever in electronic formats, and also stand to benefit more than ever from ongoing advances in the medical and epidemiological sciences. At the same time, this growing body of knowledge as well as volatile world events present an increasingly complex set of threats to population health. As a consequence, public health officials are finding that they need to ask many more, and more complex, questions of their data in order to keep sight of the state of the public’s health. Most current disease surveillance systems enable users to ask many different questions of health data, but are limited in that users can only extract results one question, or query, at a time.

Objective

Develop an Automated Data Query tool to allow public health officials to easily extract batches of raw medical encounter data using custom queries that the officials themselves set up. Additionally, the tool shall be capable of running anomaly detection algorithms against the raw data and returning the statistics. Users shall be able to perform their own analyses on the data and/or the statistical results after using the tool to collect the information efficiently. The tool will help them spot trends of interest that may be specific to their own jurisdictions.

Evidence suggests that transmission within the workplace contributes significantly to the magnitude of a pandemic flu epidemic. A significant number of large organizations have a pandemic plan in place which may help in controlling this manner of transmission. These plans typically include telecommuting and other measures to reduce the need to physically commute to the workplace. Good data are needed in order to obtain valid results from simulation models and to be able to assess the effect of reductions in commuting.

Objective

The objective in this study was to explore data on employment and commuting from different sources, using statistical analytic techniques together with geographical experts to obtain information to be provided to modelers in order to help them improve the employment and commuting component of their models, determine potential issues related to these data, and identify problem areas where further investigation is needed.

SaTScan is a freely available software that uses the scan statistic to detect clusters in space, time or space-time. SaTScan uses Monte Carlo hypothesis testing in order to produce a p-value for the null hypothesis that no clusters are present. Monte Carlo hypothesis testing can be a powerful tool when asymptotic theoretical distributions are inconvenient or impossible to discover; the main drawback to this approach is that precision for small p-values can only be obtained through greatly increasing the number of Monte Carlo replications, which is both  computer-intensive and time consuming. Depending on the type of analysis being done, the number of geographical areas included, the amount of historical data, and the number of Monte Carlo replications, SaTScan can take anywhere from seconds to hours to run. In doing daily surveillance of many syndromes, we need to limit the amount of time it takes to generate each p-value while still retaining enough precision in the p-value to determine how unusual a cluster is. Since the type of analysis done and the geographic regions being used cannot be changed in most cases, we focus here on trying to reduce the number of Monte Carlo replicates needed.

Objective

Our goal was to increase the precision of the p-value produced from SaTScan while reducing the amount of CPU time needed by decreasing the number of Monte Carlo replicates.