ISDS Conference

Irregularly shaped spatial disease clusters occur commonly in epidemiological studies, but their geographic delineation is poorly defined. Most current spatial scan software usually displays only one of the many possible cluster solutions with different shapes, from the most compact round cluster to the most irregularly shaped one, corresponding to varying degrees of penalization parameters imposed to the freedom of shape. Even when a fairly complete set of solutions is available, the choice of the most appropriate parameter setting is left to the practitioner, whose decision is often subjective.

Objective

We propose a novel approach to the delineation of irregularly shaped disease clusters, treating it as a multi-objective optimization problem. We present a new insight into the geographic meaning of the cluster solution set, providing a quantitative approach to the problem of selecting the most appropriate solution among the many possible ones.

Influenza is a significant public health problems in the US leading to over one million hospitalizations in the elderly population (age 65 and over) annually. While influenza preparedness is an important public health issue, previous research has not provided comprehensive analysis of season-by-season timing and geographic shift of influenza in the elderly population. These findings fail to document the intricacies of each unique influenza season, which would benefit influenza preparedness and intervention. The annual harmonic regression model fits each season of disease incidence characterized by its own unique curve. Using this model, characteristics of the seasonal curve for each state and each season can be compared. We hypothesize that travelling waves of influenza in the 48 contiguous states differ dramatically in each influenza season.

Objective

In surveillance it is imperative that we know when and where a disease first begins. The objective of this study was to examine trends in traveling waves of influenza in the US elderly population. Preparedness for influenza is an important yet difficult public health goal due to variability in annual strains, timing, and shift of the influenza virus. In order to better prepare for influenza epidemics, it is important to assess seasonal variation across individual influenza seasons on a state-by-state basis. This approach will lead to effective interventions especially for susceptible populations such as the elderly.

In the Northern part of Norway, all General Practitioners (GPs) and hospitals use electronic health records (EHR). They are connected via an independent secure IP-network called the Norwegian Health Network. The newly developed “Snow Agent System” can utilize this environment by distributing processes to, and extracting epidemiological data directly from, the EHR system in a geographic area. This system may enable the GPs to discover local disease outbreaks that may have affected the current patient by providing epidemiological data from the local population. Currently, work is being done to add more functionality to the system. The overall goal for this project is to contribute to a system that will share epidemiological information between GPs and provide them with information about contagious diseases that may be useful in a clinical setting.

To achieve this, we need the GPs to accept and use the system. Nearly one half of information systems fail due to user resistance and staff interference despite the fact that they are technologically sound. One of the reasons for user resistance is lack of user involvement and bad design. The more specialized the system, the more you need user research to unsure success. With this in mind we have decided to take a User-Centred-Design approach to the project.

Objective

The Norwegian Centre for Telemedicine plans to establish a peer-to-peer symptom based surveillance network between all GPs, laboratories, accident and emergency units, and other relevant health providers in Northern Norway. This paper describes some initial results from a study of GPs’ user requirements, regarding what they want in return from the system.

NC DETECT is the Web-based early event detection and timely public health surveillance system in the North Carolina Public Health Information Network. The reporting system also provides broader public health surveillance reports for emergency department visits related to hurricanes, injuries, asthma,  vaccine-preventable diseases, environmental health and others. NC DETECT receives data on at least a daily basis from four data sources: emergency departments, the statewide poison center, the statewide EMS data collection system, a regional wildlife center and laboratory data from the NC State College of Veterinary Medicine. Data from select urgent care centers are in pilot testing.

Objective

Managers of the NC DETECT surveillance system wanted to augment standard tabular Web-based access with a Web-based spatial-temporal interface to allow users to see spatial and temporal characteristics of the surveillance data. Users need to see spatial and temporal patterns in the data to help make decisions about events that require further investigation. The innovative solution using Adobe Flash and Web services to integrate the mapping component with the backend database will be described in this paper.

BioSense data includes Department of Defense and Veterans Affairs ambulatory care diagnoses and procedures, as well as Laboratory Corporation of America lab test orders. Data are mapped to eleven syndrome categories. SaTScan is a spatio-temporal technique that has previously been applied to surveillance at the metropolitan area level. Visualization of national results involves unique issues, including displaying cluster information that crosses jurisdictions, zip codes with highly variant data volume, and evaluating large multiple state clusters. SaTScan was first implemented in June 2005 in the BioSense application for daily monitoring at CDC’s BioIntelligence Center.

Objective

The objective is to describe the visualization and monitoring of the national spatio-temporal SaTScan results in the BioSense application. This is the first application of this algorithm to a national early event detection and situational awareness system.

Syndromic Surveillance has been in use in New York City since 2001, with 2.5 million visits reported from 39 participating emergency departments, covering an estimated 75% of annual visits. As syndromic surveillance becomes increasingly spatial and tied to geography, the resulting spatial analysis is also evolving to provide new methodology and tools. In late 2004, the New York City Department of Health and Mental Hygiene (DOHMH) created the geographic information systems (GIS) Center of Excellence to identify ways in which GIS could enhance programs like syndromic surveillance. The DOHMH uses the SaTScan program for much of its spatial analysis (i.e. cluster analysis).

Objective

This paper describes a series of visualization enhancements and automation processes to efficiently depict syndromic surveillance data in GIS. Modelling the portrayal of events when merging existing syndromic surveillance with GIS can standardize and expedite results.

The CDC recently developed sub-syndromes for classifying disease to enhance syndromic surveillance of natural outbreaks and bioterrorism. They have developed ICD9 classifiers for six GI Illness subsyndromes: Abdominal Pain, Nausea and Vomiting, Diarrhea, Anorexia, Intestinal infections, and Food poisoning. If the number of visits for sub-syndromes varies significantly by age it may impact the design of outbreak detection methods.

Objective

We hypothesized that the percentage of visits for the GI sub-syndromes varied significantly with age.

Objective

To study if syndromic surveillance would have an added value over existing surveillance systems, we retrospectively evaluated whether known trends in respiratory pathogens are reflected in medical registrations in the Netherlands when using respiratory syndrome groupings.

In addition to utilizing syndromic surveillance data to respond to public health threats and prepare for major incidents, local health departments can utilize the data to examine patient volumes in the emergency departments (EDs) of local hospitals. The information obtained may be valuable to hospital and clinic administrators who are charged with allocating resources. 

Indianapolis represents 92% of Marion County’s population. The county’s public hospital and clinic network provide care for 1 in 3 county residents who are Medicaid enrollees or uninsured. To assess the need for extended hours at eight public primary care clinics in Marion County, Indiana, this study examined the hospital’s ED volume. We hypothesize that

changes in the ED volume trends that corresponded to the start or end of usual clinic hours (8am-5pm) would be evidence of clinic hours’ impact on ED use.

Objective

This paper highlights the use of syndromic surveillance data to examine daily trends in ED volume at an urban public hospital.

In 2003, the need for a system to track and manage patient status and location was identified by Boston Emergency Medical Services (Boston EMS) and the Conference of Boston Teaching Hospitals. After consultation with EMS (municipal, fire based, and private), hospital, local and state public health and emergency management stakeholders, a core group from Boston EMS and Boston Public Health Commission (BPHC) developed guidelines for a Metro Boston Patient Tracking System. The goal was to provide a system to reunite family members and serve as a tool for managing short term/high impact mass casualty incidents and protracted disease outbreaks.

Since 2004, BPHC Communicable Disease Control Division (CDC) has effectively managed several mass prophylaxis clinics in response to infectious disease outbreaks. However patient data was largely collected on paper based forms, limiting the availability of real-time clinic data to incident command. To address these challenges BPHC CDC began meeting with Boston EMS to define the business processes and information needs during public health emergencies.

Objective

To describe the electronic patient tracking system configured by Boston EMS and the BPHC CDC to address information needs during public health emergencies.