Using geographical information systems mapping to identify areas presenting high risk for traumatic brain injury

Abstract

Background: The aim of this study is to show how geographical information systems (GIS) can be used to track and compare hospitalization rates for traumatic brain injury (TBI) over time and across a large geographical area using population based data.

Results & discussion: Data on TBI hospitalizations, and geographic and demographic variables, came from the Ontario Trauma Registry Minimum Data Set for the fiscal years 1993-1994 and 2001-2002. Various visualization techniques, exploratory data analysis and spatial analysis were employed to map and analyze these data. Both the raw and standardized rates by age/gender of the geographical unit were studied. Data analyses revealed persistent high rates of hospitalization for TBI resulting from any injury mechanism between two time periods in specific geographic locations.

Conclusions: This study shows how geographic information systems can be successfully used to investigate hospitalizaton rates for traumatic brain injury using a range of tools and techniques; findings can be used for local planning of both injury prevention and post discharge services, including rehabilitation.

Figure 1

Uses of GIS to inform public health decision-making. Derived from the "exploratory spatial data analysis" process of Dragićević et al. [27]

Figure 2

Examples of maps showing visualization, exploratory data analysis, and geographic (spatial) analysis. 2a: Example of visualization. Providing an overview and visual illustration of data sets, and putting them in geographic context, is an important function of GIS and mapping. Here we view an overall look at the distribution of cases of TBI from the 1993-94 data set. 2b: Example of exploratory data analysis. Maps of ratios or proportions of subsets of the participant population, by different variables, can be created for visual examination. The cartographic methods used for representing data have a significant impact on their visual interpretation. Many municipalities are small in area, and so are practically invisible in the choropleth map shown here. 2c: Example of geographic (spatial) analysis: Statistics such as the "Local Moran's I", generically referred to as the Local Index of Spatial Autocorrelation (LISA) can be used to identify areas of significant geographic clustering of data points, in this case, the initial calculation of standardized morbidity ratios by CSD for TBI.

Figure 3

Ontario Census subdivisions (CSDs) in 1991 (n = 951), and 2001 (n = 586).

Figure 4

Examples of mapping of TBI rates and cluster analyses.

Figure 5

Persistent high clusters for 1993-94 and 2001-02 data, identified by LISA and Getis-Ord Gi* cluster analyses.

Figure 6

LISA Cluster maps contrasting results using 1993-94 data with 2001-02 data, each aggregated to 2001-comparable CSDs. High-low LISA clusters in Southern Ontario show similar patterns, but in different CSDs.