Mapping

The ability to rapidly detect any substantial change in disease incidence is of critical importance to facilitate timely public health response and, consequently, to reduce undue morbidity and mortality. Unlike testing methods (1, 2), modeling for spatio-temporal disease surveillance is relatively recent, and this is a very active area of statistical research (3). Models describing the behavior of diseases in space and time allow covariate effects to be estimated and provide better insight into etiology, spread, prediction and control. Most spatio-temporal models have been developed for retrospective analyses of complete data sets (4). However, data in public health registries accumulate over time and sequential analyses of all the data collected so far is a key concept to early detection of disease outbreaks. When the analysis of spatially aggregated data on multiple diseases is of interest, the use of multivariate models accounting for correlations across both diseases and locations may provide a better description of the data and enhance the comprehension of disease dynamics.

Objective

This study deals with the development of statistical methodology for on-line surveillance of small area disease data in the form of counts. As surveillance systems are often focused on more than one disease within a predefined area, we extend the surveillance procedure to the analysis of multiple diseases. The multivariate approach allows for inclusion of correlation across diseases and, consequently, increases the outbreak detection capability of the methodology

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.

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 global health threat of highly pathogenic avian influenza H5N1 has been increasing rapidly in the world since the crosscountry outbreaks during 2003-04. In South and East Asia, the human influenza A (H3N2) was proved to be seeded there with occurring annual cases. Intensive surveillance of influenza is the most urgent strategy to avoid large-scale epidemics and high case fatality rates. Sentinel physicians’ surveillance is the most sensitive mechanism to reflect the health status of community people. In France and Japan, comprehensive sentinel-physician surveillance systems were set up and geographic information system was applied to display the diffusion patterns of influenza-like illness. Kriging method, which was used to display the diffusion, was hard to monitor the multiple temporal and spatial dimensions in one map. Therefore, Ring maps were proposed to overcome this difficulty.

Objective

This study describes a visualizing ring maps to monitor the alert levels of Influenza-like illness, and provide possible insights of temporal and spatial diffusion patterns in epidemic and nonepidemic seasons.

To inform health professionals and the public directly about real-time utilization of local Emergency Departments for respiratory and gastrointestinal illness to enable enhanced communication and collaboration between Public Health and health care workers.

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.

New York City ED syndromic surveillance data uses SaTScan to detect spatial signals. SaTScan analysis has been integrated into SAS since 2002, and signal maps have been generated from SAS since 2003. Signal maps are created occasionally to investigate a severe outbreak based on the SaTScan results. Previous use and integration of additional GIS analysis in ArcGIS has been done manually, requiring more time, and running the risk of being less consistent than an automated method. This script now integrates the SAS, SaTScan and spatial analysis from ArcGIS to create high-quality maps in an automated procedure.

Objective

The objective was to minimize the amount of time spent on routine, daily analysis of syndromic data, integrate additional spatial analysis, create better maps, and cut response times to outbreaks.