ISDS Conference

The former Soviet Union (FSU)—through the Sanitary-Epidemiologic Service (SES)—developed an extensive system of disease surveillance that was effective, yet centrally planned in Moscow. Even after the fall of the FSU in 1991, most newly independent states maintained all or parts of the SES structure. However, even 15 years later, the loss of economic and technical assistance from Moscow has negatively impacted the effectiveness and efficiency of disease surveillance in these republics, including Armenia and Georgia. In 2005, Armenia and Georgia reported tuberculosis (TB) incidences of 71 and 83, respectively, per 100,000.

Objective

To enhance its effectiveness and efficiency, we evaluated TB surveillance in the FSU Republics of Armenia and Georgia.

The North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS) receives daily emergency department (ED) data from 33 (29%) of the 114 EDs in North Carolina. These data are available via a Web-based portal and the Early Aberration Reporting System to authorized NC public health users for the purpose of syndromic surveillance (SS). Users currently monitor several syndromes including: gastrointestinal severe, fever/rash illness and influenza-like illness. The syndrome definitions are based on the infection-related syndrome definitions of the CDC and search the chief complaint (CC) and, when available, triage note (TN) and initial temperature fields. Some EDs record a TN, which is a brief text passage that describes the CC in more detail. Most research on the utility of ED data for SS has focused on the use of CC. The goal of this study was to determine the sensitivity, specificity, and both positive and negative predictive value of including TN in the syndrome queries.

Objective

This study evaluates the addition of TN to syndrome queries used in the NC BEIPS.

We have previously shown that timeliness of detection is influenced both by the data source (e.g., ambulatory vs. emergency department) and demographic characteristics of patient populations (e.g., age). Because epidemic waves are thought to move outward from large cities, patient distance from an urban center also may affect disease susceptibility and hence timing of visits. Here, we describe spatial models of local respiratory illness spread across two major metropolitan areas and identify recurring early hotspots of risk. These models are based on methods that explicitly track illness as a traveling wave across local geography.

Objective

To characterize yearly spatial epidemic waves of respiratory illness to identify early hotspots of infection.

San Diego County Public Health has been conducting syndromic surveillance for the past few years. Currently, the system has become largely automated and processes and analyzes data from a variety of disparate sources including hospital emergency departments, 911 call centers, prehospital transports, and over-the-counter drug sales. What has remained constant since the system’s initial conceptualization is the local opinion that the data should be analyzed and interpreted in a variety of ways, in anticipation for the variety of contexts in which events that are of public health interest may unfold. Relatively small increases in volume that are sustained over time will likely be detected by methods designed to detect “small process shifts”, and include the CUSUM and EWMA methods. Larger increases in volume that are not sustained over time will likely be detected by other employed methods (P-Chart in the event of a non-proportional increase in volume, U-Chart in the event of a proportional increase in volume). A retrospective analysis was conducted on historical data from various data sources to determine the frequency of signals and detected events as well as the context within which the alert occurred (i.e., the “shape” of the data). Findings regarding several actual public health events will also be discussed.

Objective

This paper describes the frequency, various “shapes” and magnitudes of data anomalies, and varying ways actual public health events may present themselves in syndromic data.

Overseas studies showed that increases in over-the-counter (OTC) drug sales might serve as an indicator of community disease outbreaks before they are detected by conventional surveillance systems. Using data collected retrospectively from commercial drug retailers, the Department of Health of Hong Kong conducted an exploratory study to examine the potential of monitoring OTC drug sales for early detection of community disease outbreaks.

Objective

This study evaluates whether OTC drug sales can serve as an earlier indicator for detecting community disease outbreaks in Hong Kong.

While there has been some work to evaluate different data sources for syndromic surveillance of influenza, no one has yet assessed the utility of simultaneously restricting data to specific visit settings and patient age-groups using data drawn from a single source population. Furthermore, most studies have been limited to the emergency departments (ED), with few evaluating the timeliness of data from community-based primary care.

Objective

Using physician billing data from a single source population, we aimed to compare age-group and visit setting specific patterns in the timing of patients presenting to community-based healthcare settings and hospital ED for influenza-like-illnesses (ILI). We thus evaluate the utility of focusing on particular age-groups and care settings for syndromic surveillance of ILI in ambulatory care.

Of critical importance to the success of syndromic surveillance systems is the ability to collect data in a timely manner and thus ensure rapid detection of disease outbreaks. Most emergency department-based syndromic surveillance systems use information rou-tinely collected in patient care including patient chief complaints and physician diagnostic coding. These sources of data have been shown to have only limited sensitivities for the identification of cer-tain syndromes. Another potential source of information, which has not been previously studied, is the patient. Studies have shown that patients as well as parents can accurately report information about their own or their child’s illness. The value of of patient and parent self-reported informa-tion for disease surveillance systems has not been measured.

Objective

To determine whether patients and their families can directly provide medical information that enables syndrome classification.

Tuberculosis (TB) has reemerged as a global health epidemic in recent years. Although several researchers have examined the use of space-time surveillance to detect TB clusters, they have not used genetic information to verify that detected clusters are due to person-to-person transmission. Using genetic fingerprinting data for TB cases, we sought to determine whether detected clusters were due to recent transmission.

Objective

This paper describes the utility of prospective spacetime surveillance to detect genetic clusters of TB due to person-to-person spread.

In order to detect influenza outbreaks, the New York State Department of Health emergency department (ED) syndromic surveillance system uses patients’ chief complaint (CC) to assign visits to respiratory and fever syndromes. Recently, the CDC developed a more specific set of “sub-syndromes” including one that included only patients with a CC of flu or having a final ICD9 diagnosis of flu. Our own experience was that although flu may be a common presentation in the ED during the flu season, it is not commonly diagnosed as such. Emergency physicians usually use a symptomatic diagnosis in preference, probably because rapid testing is generally unavailable or may not change treatment. The flu subsyndrome is based on a specific ICD9 code for influenza. It is unknown whether patient visits that meet these restrictive criteria are sufficiently common to be of use, or whether patients who identify themselves as having the flu are correct.

Objective

Our objective was to examine the CC and ICD9 classifiers for the influenza sub-syndrome to assess the frequency of visits and the agreement between the CC, ICD9 code and chart review for these patient visits.

States and localities are using biosurveillance for a variety purposes including event detection, situational awareness, and response. However, little is known about the impact of biosurveillance on the operational components and functioning of the public health system and the added value of biosurveillance to traditional surveillance methods. A deeper understanding of how state and local public health systems use biosurveillance data and the factors that facilitate and impede its utility are needed to inform efforts to improve public health surveillance.

Objectives

A goal of the case studies was to assess the impact of biosurveillance on public health system preparedness, detection and response for a range of public health threats.