G. E. Sorensen1, M. Aranke1, M. Bhatia1, S. Yang2, D. Vyas3 1Texas Tech University Health Science Center School Of Medicine,Lubbock, TX, USA 2Texas Tech University Health Science Center,Pathology,Lubbock, TX, USA 3Texas Tech University Health Science Center,Surgery,Odessa, TX, USA
Introduction:
Rural areas often lack sufficient first responder densities (FRD) which can result in decreased pre-hospital care that a trauma victim receives. Prompt, well-executed pre-hospital care by first responders can lead to a reduction in motor vehicle mortalities. The importance of first responders is widely agreed upon by the healthcare and public health community, yet no mathematical model currently exists that gives a reliable estimate of the number of first responders a certain community needs to provide improved pre-hospital care. The objectives of our study were to quantify the relationship between FRD and state census data, and to develop a model that will effectively estimate the number of responders needed to reduce motor vehicle accident mortality rate.
Methods:
Data was collected from state census databanks for all 50 states and subset into urban and rural. A total of 10 rural area variables were used in the analysis which include: population density, first responders (firemen and EMTs), total state area (sq. km), number of total hospitals, hospital density (km), surgeon density, total rural road density, poverty density, median household income, and motor vehicle accident mortality rate. Initial relationships among the variables was determined using a Pearson Correlation Coefficient. A multiple regression analysis was used to estimate FRD based on a subset of significant variables from the correlation analysis. A simple regression was then used to determine the direct relationship between FRD and motor vehicle mortality rate.
Results:
The top model estimating FRD included hospital density, poverty density, and median household income (Adj. R-Sq = 0.96; P<0.001). Thus, as hospital density, poverty density, and median household income increased, there was an increase in first responder density. There is an inverse relationship between FRD and motor vehicle mortality rate (Adj. R-Sq = 0.54; P<0.001).
Conclusion:
Our models demonstrate that FRD in rural areas was a function of the number of hospitals as well as income status (i.e. poverty and household income). Furthermore, as FRD increased, motor vehicle mortality rates decreased. These models hold the potential for determining which rural areas lack the appropriate level of pre-hospital care which warrant the need to increase pre-hospital education and the number of first responders. The models are simple and could be expanded, however our goal was to develop a nationwide preliminary model that used FRD and motor vehicle mortality to address pre-hospital care. Further modeling efforts could elucidate additional regional factors and ultimately, lead to better allocation of public health resources with the overall goal of reducing MVA mortality rate. This study will enable us to engage policy makers and allow states to make informed decisions about appropriate resources for training/education for first responders to reach the goal of improving rural pre-hospital trauma care.