ISDS Conference

By capturing the spatio-temporal organization of the data using a graph, GraphScan avoids the challenges associated with trying to “fit” incoming data into moving windows of predefined shapes and sizes. Whereas the popular space-time permutation scan statistic [1] attempts to find clusters within spacetime volumes of predefined shape, GraphScan employs no such preconceptions about the form of the clusters.  Instead, clusters are allowed to “evolve” freely to better reflect the structural properties of the data.  Moreover, GraphScan is capable of tracking possible causal relationships between spatio-temporal events.

Objective

This paper proposes an efficient and flexible algorithm applicable to spatio-temporal aberration detection in public health data.

 Following the development of an introductory Continuing Education (CME) course in syndromic surveillance, the Education and Training Committee of the International Society for Disease Surveillance recognized the need to educate future non-medical public health workers and reviewed courses offered by the top five public health schools recognized by US News and World Report1.  All public health schools offered courses that included information on public health practice and infectious disease epidemiology and few offered courses on spatial and disaster epidemiology with attention given to syndromic surveillance, but none of the schools offered a comprehensive course that integrated topics of public health practice, infectious disease surveillance, data management and analytic techniques, disaster preparedness, and syndromic surveillance2-6.  The development of the graduate school course builds on our existing CME slide set goals that teaches students about syndromic surveillance and presents the course in a free and easy to use format for all schools of public health.  The ISDS hopes the semester long course will be taught by ISDS members in each state to spread awareness and knowledge on the topic of syndromic surveillance.

Objective

The paper describes the development of a graduate-level course to teach future non-medical public health workers about syndromic surveillance.

Health care workers (HCWs) have an increased risk of exposure to infectious agents including (among others) tuberculosis, influenza, norovirus, and Clostridium difficile as a consequence of patient care1,2 Most occupational transmission is associated with violation of one or more basic principles of infection control: handwashing; vaccination of HCWs; and prompt isolation.3 OH surveillance is paramount in guiding efforts to improve worker safety and health and to monitor trends and progress over time.4 GIS can assist in supporting health situation analysis and surveillance for the prevention and control of health problems, for example: by creating temporal-spatial maps of outbreaks, public health workers can visualize the spread of cases as the outbreak progresses; spatial/database queries allow for selection of a specific location or condition to focus public health resources.

Objective

This paper describes a GIS tool which maps the floors and departments of a Southeastern Ontario tertiary care hospital for the purpose of monitoring respiratory and gastrointestinal (GI)-related Occupational Health (OH) visits among hospital employees.

The inception of syndromic surveillance has spawned a great deal of research into emergency department chief complaint data. In addition to its use as an early warning system of a bioterror or outbreak event, many health departments are attempting to maximize the utility of the information to augment chronic and communicable disease surveillance. Hence, it can be used to enhance the traditional methods of surveillance. Using syndromic data to describe what could be the normal for a geographic area may be useful in monitoring a population for disease trends. Prevention efforts could be concentrated during a particular time of year. In addition, geospatial shifts in directional trends may indicate an unusual occurrence related to the utilization of emergency department services.

Objective

To describe the geographical mean as well as the directional trends of syndromes for the District of Columbia using temporal and geospatial analyses.

As part of public health protection activities conducted in support of the G8 Summit in Sea Island, GA, June 2004, DPH implemented SS in the state’s coastal region using information provided from ED visits, 911 calls, and pharmacy sales. Following this high-profile event, questions arose about whether to maintain the ED system and about whether and where to extend its use in GA.  Despite the emergence of practice-based guidance for conducting SS and the growing experience of public health agencies, little guidance is available regarding strategies for identifying sites where SS should be targeted.

 

Objective

This paper describes the strategy used by the Georgia Division of Public Health (DPH) in implementing syndromic surveillance (SS), including criteria for prioritizing localities and the early results of applying these criteria in initiating new emergency department (ED)-visit based systems.

Many heuristics were developed recently to find arbitrarily shaped clusters (see  review  [1]). The most popular statistic is the spatial scan  [2]. Nevertheless, even if all cluster solutions could be known, the problem  of selecting the best cluster is ill posed. This happens because other measures, such as geometric regularity  [3-5] or topology  [6] must be taken intoconsideration. Most cluster finding  methods does not address  this last problem. A genetic multi-objective algorithm was developed elsewhere to identify irregularlyshaped clusters [5]. That method conducts a search aiming to maximize two objectives, namely the scan  statistic and the regularity of shape (using the compactness concept).The solution presented is a Pareto-set, consisting of all the clusters found which are not simultaneously worse in both objectives. The significance evaluation is conducted in parallel for all the  clusters  in  the  Pareto-set  through a  Monte Carlo simulation, determining the best cluster solution.

Objective

Irregularly shaped clusters occur naturally in disease surveillance, but they are not well defined. The number of possible clusters increases exponentially with the number of regions in a map. This concurs to reduce the power of detection, motivating the utilization of some kind of penalty function to avoid excessive freedom of shape. We introduce a weak link based correction which penalizes inconsistent clusters, without forbidding the presence of the geographically interesting irregularly shaped ones.

Heat surveillance in Houston is currently limited to mortality reports from the medical examiners office. A possible source of heat related morbidity is the Houston Real-time Outbreak Disease Surveillance (RODS) system. The RODS system was put into practice in the Houston Department of Health and Human services (HDHHS) in 2004 and now encompasses 37 hospitals. While initially designed for early detection of bioterrorism events, using syndromic data to detect other medical complaints, such as heat related morbidity, could prove to be beneficial and cost-effective for large cities, such as Houston.

Objective

The purpose of this investigation is to determine the value of using the RODS system to track heat-related morbidity in Houston, Texas.

BioSense is a CDC initiative to promote situational awareness through summarizing, analyzing, and presenting health related event information. Among the data sources collected and analyzed through the BioSense application are the Department of Defense and Department of Veterans Affairs ambulatory clinic care data. Clinical diagnoses and procedures are quantified, and analytic results are presented and categorized into 94 state and metropolitan areas.

Objective

Precise geographic location of health events is a challenging but critical component to determine the likely site of exposure for disease surveillance. This paper describes a method used by BioSense to develop and implement a reasonable set of rules in defining geographic locations of health events.

I Medical services for outpatients are well developed due to universal public health insurance. Even patients who have mild symptoms can visit a clinic freely in Japan. Thus the monitoring of outpatients provides very timely information to detect unusual events. On the other hand, EMRs haven't had much penetration, less than 10% at clinics and 20% at hospitals. Moreover, almost nobody uses HL7 or other standards for EMRs. Therefore, it is very difficult to develop a syndromic surveillance system using EMRs like the U.S. We have to develop a system for each EMR and it has a heavy cost. In Japan, there are about 40 thousand pharmaciesand almost half of drugs prescribed are delivered through pharmacies. Almost all pharmacies record prescriptions electronically. Objective: So that full automatic syndromic surveillance cover the whole of nation, we construct the system using the information of prescription.

Objective

We asked to what extent computerized processing of the full free-text clinical documentation could enhance syndrome detection compared to the sole use of structured data elements from a comprehensive electronic medical record.