ISDS Conference

Syndromic surveillance systems can detect increases in respiratory and gastrointestinal illness, but diagnosis of etiologic agents can be delayed due to difficult, time-consuming identification and low rates of testing for viral pathogens. Rapid diagnostic (RD) assays may aid in early identification and characterization of large outbreaks by allowing decision makers to “rule in” or “rule out” potential etiologic agents.

Objective

This paper describes preliminary results and implementation lessons learned from a RD testing pilot project. The project’s purpose is to prospectively collect diagnostic data on common causes of community-wide illness in order to supplement syndromic surveillance in New York City.

The BioSense system currently receives real-time data from more than 370 hospitals, as well as national daily batched data from over 1100 Department of Defense and Veterans Affairs medical facilities. BioSense maps chief complaint and diagnosis data to 11 syndromes and 78 sub-syndromes (indicators). One of the 11 syndromes is gastrointestinal (GI) illness and 6 of the subsyndromes (abdominal pain; anorexia, diarrhea, food poisoning, intestinal infections, ill-defined; and nausea and vomiting) represent gastrointestinal concepts.

Objective

To describe the potential use of BioSense chief complaint and final diagnosis data for GI illness surveillance.

Every public health monitoring operation faces important decisions in its design phase. These include information sources to be used, the aggregation of data in space and time, the filtering of data records for required sensitivity, and the design of content delivery for users. Some of these decisions are dictated by available data limitations, others by objectives and resources of the organization doing the

surveillance. Most such decisions involve three characteristic tradeoffs: how much to monitor for exceptional vs customary health threats, the level of aggregation of the monitoring, and the degree of automation to be used.

The first tradeoff results from heightened concern for bioterrorism and pandemics, while everyday threats involve endemic disease events such as seasonal outbreaks. A system focused on bioterrorist attacks is scenario-based, concerned with unusual diagnoses or patient distributions, and likely to include attack hypothesis testing and tracking tools. A system at the other end of this continuum has broader syndrome groupings and is more concerned with general anomalous levels at manageable alert rates. 

Major aggregation tradeoffs are temporal, spatial, and syndromic. Bioterrorism fears have shortened the time scale of health monitoring from monthly or weekly to near-real-time. The spatial scale of monitoring is a function of the spatial resolution of data recorded and allowable for use as well as the monitoring institution’s purview and its capacity to collect, analyze and investigate localized outbreaks.

Automation tradeoffs involve the use of data processing to collect information, analyze it for anomalies, and make investigation and response decisions. The first of these uses has widespread acceptance, while in the latter two the degree of automation is a subject of ongoing controversy and research. To what degree can human judgment in alerting/response decisions be automated? What are the level and frequency of human inspection and adjustment? Should monitoring frequency change during elevated threat conditions?

All of these decisions affect monitoring tools and practices as well as funding for related research.

Objective

This purpose of this effort is to show how the goals and capabilities of health monitoring institutions can shape the selection, design, and usage of tools for automated disease surveillance systems.

In the Northern part of Norway, all general practitioners (GPs) and hospitals use electronic health record systems. They are all connected via an independent secure IP-network called the Norwegian Health Network which enables electronic communication between all institutions involved in disease prevention and healthcare.

Objective

The Norwegian Centre for Telemedicine plans to establish a peer-to-peer based surveillance  network between all GPs, laboratories, accident and emergency units, and other relevant health providers and authorities in Northern Norway. This paper briefly describes the architecture and components of the system and the motivation for using this approach.

SaTScan is a program often used for space-time cluster detection. In order to run SaTScan, the data must be in a pre-specified text format. Once the input files are in the correct format, the typical user opens SaTScan, chooses the appropriate options, and runs SaTScan. The output from SaTScan consists of one or more text files with statistical and geographical information about the clusters. Errors in SaTScan often require re-extraction of the data into the specified text format.

When running SaTScan many times per day, as is commonly done in surveillance, it can be cumbersome to create all of the necessary data sets and run SaTScan. This is also true for any kind of evaluation of systems that rely on SaTScan for surveillance. In addition, the lack of graphical output, such as a map of the areas identified in the cluster, detracts from the utility of otherwise excellent software.

Objective

The purpose of this project was to create a SAS (SAS Institute, Cary, NC) interface for SaTScan which can be used to create the necessary input files, run SaTScan directly from SAS (without using SaTScan’s GUI), and to combine the output with geographic boundary files to create a single-page output containing a map and statistics describing the resulting clusters found by SaTScan.

National Retail Data Monitor (NRDM) is a public health surveillance tool that collects and analyzes daily sales data for over-the-counter (OTC) health-care products from >15,000 retail stores nationwide. This is a system developed by Real-Time Outbreak and Disease Surveillance Laboratory. NRDM has been in continuous operation since December 2002. The Washoe County District Health Department implemented this system in November 2003. During initial phase of implementation, NRDM was used retrospectively on as-needed basis. Since September 2004, monitoring NRDM for volume of OTC sales for anti-diarrhea medications became a daily routine.

Objective

The objective of this paper is to evaluate the role of NRDM in gastrointestinal illness outbreak investigation in Washoe County, Nevada. The evaluation will focus on usefulness of system, sensitivity, positive predictive value, representativeness, and timeliness followed by updated CDC guidelines.

Analysis of time series data requires accurate calculation of a predicted value. Non-regression methods such as the Early Aberration Reporting System CuSum are computationally simple, but most do not adjust for day of week or holiday. Alternately, regression methods require larger counts, more computer resources, and possibly longer baseline periods of data. As increasing volumes of data are reported and analyzed, the predictive accuracy of simpler methods should be assessed and optimized.

Objective

To compare the predictive accuracy of three non-regression methods in analysis of time series count data.

In 2004, the NSW Public Health Real-time Emergency Department Surveillance System operating in and around Sydney, Australia signalled a large-scale increase in Emergency Department (ED) visits for gastrointestinal illness (GI). A subsequent alarming state-wide rise in institutional gastroenteritis outbreaks was also seen through conventional outbreak surveillance.

Objectives

To examine the association between short-term variation in ED visits for GI with short-term variation in institutional gastroenteritis outbreaks and thus to evaluate whether syndromic surveillance of GI through EDs provides early warning for institutional gastroenteritis outbreaks.

Influenza is an important public health problem associated with considerable morbidity and mortality. A disease traditionally monitored via legally mandated reporting, researchers have identified alternative data sources for influenza surveillance. The hospital environment presents a unique opportunity for comparative studies of biosurveillance data with high quality and various level of clinical information ranging from provisional diagnoses to laboratory confirmed cases. This study investigated the alert times achievable from hospital-based sources relative to reporting of influenza cases. The earlier detection of influenza could potentially provide more advanced warning for the medical community and the early implementation of precautionary measures in vulnerable populations.

Objective

To determine the relative alert time of influenza surveillance based on hospital data sources compared to notifiable disease reporting.

The Centers for Disease Control and Prevention BioSense has developed chief complaint (CC) and ICD9 sub syndrome classifiers for the major syndromes for early event detection and situational awareness. The prevalence of these sub-syndromes in the emergency department population and the performance of these CC classifiers have been little studied. Chart reviews have been used in the past to study this type of question but because of the large number of cases to review, the labor involved would be prohibitive. Therefore, we used an ICD9 code classifier for a syndrome as a surrogate by chart reviews to estimate the performance of a CC classifier.

Objective

To determine the prevalence of the sub-syndromes based on the ICD9 classifiers, and to determine the sensitivity, specificity, positive predictive value and negative predictive value of CC classifiers for the sub-syndromes associated with the respiratory and gastrointestinal syndromes using the ICD9 classifier as the criterion standard.