Using GIS and spatial statistics to explore and model demand for emergency medical services in the city of Sudbury, Ontario

The purpose of this research is to examine the nature of the relationship between EMS ambulance call volume and demographic, socioeconomic and geographic (urban structural) forces in the City of Sudbury, a medium sized city of approximately 100,000 persons in Ontario, Canada. As in past research in the area of EMS demand, linear regression is used to model this relationship. However, unlike previous work, spatial autocorrelation inherent in real world data is addressed to mitigate violation of the assumption of independence required for classic regression. Using a Geographical Information System (ArcView 3.2) EMS data are geolocated onto a spatial framework for which 1996 census data are available. A spatial analysis program (SpaceStat 1.90) is used to operationalize a spatial model, and perform a battery of spatial diagnostics. After exploring the data with an aspatial stepwise regression model and identifying spatial autocorrelation in the explanatory variables, a mixed aspatial/spatial stepwise regression model is used. The variables Percent People Living Alone and its spatially lagged version, a lagged version of Percent of Apartment Dwellings and lagged Percent of People Aged 20 to 64 account for 52% of the variation in EMS calls per 1,000 persons. Clearly, demand for Emergency Medical Services varies greatly from place to place within a community. And this variety is, in part at least, related to underlying demographic and socioeconomic realities. Strategic deployment of resources based on these realities could assure provision of more effective and efficient Emergency Medical Services. Also, injury prevention and health promotion programs could be targeted more precisely to groups and areas in need through the help of EMS demand analysis