A Model for Identifying and Ranking Need for Trauma Service in Nonmetropolitan Regions Based on Injury Risk and Access to Services

Abstract

Timely access to definitive trauma care has been shown to improve survival rates after severe injury. Unfortunately, despite development of sophisticated trauma systems, prompt, definitive trauma care remains unavailable to over 50 million North Americans, particularly in rural areas. Measures to quantify social and geographic isolation may provide important insights for the development of health policy aimed at reducing the burden of injury and improving access to trauma care in presently under serviced populations. Indices of social deprivation based on census data, and spatial analyses of access to trauma centers based on street network files were combined into a single index, the Population Isolation Vulnerability Amplifier (PIVA) to characterize vulnerability to trauma in socioeconomically and geographically diverse rural and urban communities across British Columbia. Regions with a sufficient core population that are more than one hour travel time from existing services were ranked based on their level of socioeconomic vulnerability. Ten regions throughout the province were identified as most in need of trauma services based on population, isolation and vulnerability. Likewise, 10 communities were classified as some of the least isolated areas and were simultaneously classified as least vulnerable populations in province. The model was verified using trauma services utilization data from the British Columbia Trauma Registry. These data indicate that including vulnerability in the model provided superior results to running the model based only on population and road travel time. Using the PIVA model we have shown that across Census Urban Areas there are wide variations in population dependence on and distances to accredited tertiary/district trauma centers throughout British Columbia. Many of the factors that influence access to definitive trauma care can be combined into a single quantifiable model that researchers in the health sector can use to predict where to place new services. The model can also be used to locate optimal locations for any basket of health services.