Abstract

Background: The ability to quickly and effectively receive medical treatment in the event of an automobile collision is one of the most important aspects in emergency medical services (EMS). Emergency medical service providers are the first to respond and manage cases related to trauma, emergency surgery, and critically injured patients. Response time for emergency medical services vehicles is especially important for areas, where travel distances are often much larger, compared to more urban areas. The importance of the present data and analysis procedures are their applicability to multiple environments, including urban settings. Methods: The present study is focused on optimization of analysis tools, and understanding the influences of different traffic-related variables, related to hospital EMS transport times for Pickens County, a county in west Alabama. Optimization of associated analysis tools is important for optimal trauma patient survivability, and as such, is directly relevant to the management of care for severely injured surgical patients. Of particular interest are the effects of variables, such as travel time, time of the day, day of the week, weather, lighting conditions, and crash severity, on the EMS response time (ERT), which are analyzed using two types of advanced regression analysis: geographically weighted regression (GWR) and global regression analysis (GRA). Results: For GWR analysis, the accuracy of the approach is improved by employing an adaptive bi-square kernel weighting function. The GWR approach is also unique because geographic location variations are quantified for local independent variables, as their effects are included. Magnitudes of variable coefficients, and variable t-statistic values provide information on the relative influences and impacts of different variables, and different variable combinations, as they are considered in pairs, triplets, and different combinations. Conclusion: The resulting effects and alterations to optimal EMS response time are provided for a wide range of travel conditions and travel time periods.

Highlights

  • Recent reports from the World Health Organization (WHO) indicate that road traffic injuries (RTIs) account for about 1.3 million deaths worldwide annually (World Health Organization, 2013; World Health Organization, 2015)

  • The geographically weighted regression (GWR) analysis approach is unique because geographic location variations are included for local independent variables as they are incorporated within the analysis, which is accomplished using an adaptive bi-square kernel weighting function

  • Adding variables time of the day and lighting conditions to the analysis reduces the coefficient estimate for travel time

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Summary

Introduction

Recent reports from the World Health Organization (WHO) indicate that road traffic injuries (RTIs) account for about 1.3 million deaths worldwide annually (World Health Organization, 2013; World Health Organization, 2015). Data are obtained from 24 RTI prevention experts, which show that poor management of time is one of the six major challenges related to preventable deaths in RTIs in the prehospital phase Another recent study by Ma, Zhang, Yan, Wang, Song and Xiong (2019) indicate that ERT, in addition to age, gender, seating position, and manner of collision, are all statistically significant in regard to the possibility of a fatality. The present study is focused on optimization of analysis tools, and understanding the influences of different traffic-related variables, related to hospital EMS transport times for Pickens County, a county in west Alabama This particular county is selected for analysis because travel distances are often much larger, and EMS Response Times are often longer, compared to more urban areas. Optimization of associated analysis tools is vital for optimal trauma patient survivability, and as such, is directly relevant to the management of care for severely injured surgical patients

Test Environment Data
Variable Selection and Resulting Data Trends
ANOVA Comparisons of GWR Results and GRA Results
Findings
Summary and Conclusions
Full Text
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