Do EMS times associate with injury severity?

A vehicle occupant injury prediction algorithm based on road crash and emergency medical data

INTRODUCTION

Advanced Automatic Collision Notification (AACN) systems are an automobile safety technology designed to reduce the number of fatalities in traffic accidents by optimizing early treatment methods. AACN systems rely on robust injury prediction algorithms, however, despite the importance of time to treatment, current injury prediction algorithms used in AACN systems do not take this critical time period time into consideration.

METHOD

This study developed a vehicle occupant injury prediction algorithm by using emergency transport time in addition to mass crash data, to determine the risk of serious injury for vehicle occupants in a road crash. Two sources of de-identified data were used: The South Australian Traffic Accident Reporting System (TARS) database and the highly detailed South Australian Serious Injury Database (SID). Firstly, the TARS data, a large statistical crash dataset, was imputed into a logistic regression analysis to produce a base injury prediction algorithm. The important effect of emergency transport time on the risk of death and serious injury was then independently quantified as an odds ratio (OR) from the SID. The ORs were converted into regression coefficients and subsequently introduced into the base injury prediction algorithm to produce an enhanced injury prediction algorithm.

RESULTS

The ORs calculated from the SID showed that the risk of death and serious injury increased with increasing transport time: 61-90 min (OR = 1.6), 91-120 min (OR = 3.3), and > 120 min (OR = 4.9), compared to a transport time of 60 min or less. An assessment of the base algorithm compared to the enhanced injury prediction algorithm through Receiver Operating Characteristic (ROC) analysis, demonstrated a prediction accuracy improvement from AUC 0.70 to AUC 0.73 when evaluating the respective algorithms. The injury prediction calculations indicate that the impact of two risk factors, transport time and age-related decline in human injury tolerance, are significant, and both have a strong influence on the increased risk of serious injury.

CONCLUSIONS

The impact of emergency transport time on the risk of fatal and serious injuries was determined from a relatively small, but data rich SID. Subsequently this was incorporated into an injury prediction algorithm constructed from the large (TARS) statistical crash data set to produce an enhanced injury prediction algorithm.

PRACTICAL APPLICATION

By adding the effect of transport time to enhance the basic injury prediction algorithm, an AACN that incorporates such an algorithm can be used to determine the probability of death or serious injury due to delayed treatment. Further, such a system can be used to improve policies and procedures to optimize emergency transport time.

Effect of emergency medical service response time on fatality risk of freeway crashes: Bayesian random parameters spatial logistic approach

Introduction

Emergency medical service (EMS) serves as a pivotal role in linking injured road users to hospitals via offering first aid measures and transportation. This paper aims to investigate the effect of emergency medical service (EMS) response time on the fatality risk of freeway crashes.

Methods

Crash injury severity data from Kaiyang Freeway, China in 2014 and 2015 are employed for the empirical investigation. A Bayesian random parameters spatial logistic model is developed for analyzing crash severity.

Results

Bayesian inference of the random parameters spatial logistic model demonstrates the importance of reducing EMS response time on minimizing the fatality risk of freeway crashes. Fatality odds would increase by 2.6% for 1 min increase in EMS response time. Additionally, vehicle type, crash type, time of day, horizontal curvature, vertical grade, and precipitation are also found to have significant effects on the fatality probability of freeway crashes.

Conclusion

It is crucial to reduce EMS response time to decrease the fatality likelihood of freeway crashes. Some countermeasures have been proposed to shorten EMS response time.

Police and hospital data linkage for traffic injury surveillance: A systematic review

This systematic review examines studies of traffic injury that involved linkage of police crash data and hospital data and were published from 1994 to 2023 worldwide in English. Inclusion and exclusion criteria were the basis for selecting papers from PubMed, Web of Science, and Scopus, and for identifying additional relevant papers using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and supplementary snowballing (n = 60). The selected papers were reviewed in terms of research objectives, data items and sample size included, temporal and spatial coverage, linkage methods and software tools, as well as linkage rates and most significant findings. Many studies found that the number of clinically significant road injury cases was much higher according to hospital data than crash data. Under-estimation of cases in crash data differs by road user type, pedestrian cases commonly being highly under-counted. A limited number of the papers were from low- and middle-income countries. The papers reviewed lack consistency in what was reported and how, which limited comparability.

On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry

BACKGROUND

Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient's condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting.

METHODS

The Swedish Trauma Registry was used to train and validate five models - Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network - in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates.

RESULTS

There were 75,602 registrations between 2013-2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patients were undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80-0.89 and AUCPR between 0.43-0.62.

CONCLUSIONS

AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population.

Understanding the role of faster emergency medical service response in the survival time of pedestrians

Fatalities and severe injuries among vulnerable road users, particularly pedestrians, are rising. In addition to the loss of life, about 6,000 annual pedestrian deaths in the U.S. cost society about $6 billion. Contrary to the assumption that all fatal pedestrian-involved crashes are similar, instantaneous death is substantially more severe than death that occurs several days after the crash. Instead of homogenizing all fatal pedestrian crashes, this study takes into account the severity of fatal injury crashes as a timeline based on the survival time of pedestrians. This study extracts valuable information from fatal crashes by examining pedestrians' survival time ranging from early death to death within 30 days of the crash. The Fatality Analysis Reporting System dataset is utilized from 2015 to 2018. The emergency medical service (EMS) response time is the key post-crash measure, while controlling for pedestrian, driver, roadway, and environmental characteristics. Notably, the response time and survival time can cause endogeneity, i.e., the response times may be shorter for more severe crashes. Due to the spatial and temporal nature of traffic crashes, to extract the association of different variables with pedestrians' survival time, a geographically and temporally weighted truncated regression with a two-stage residual inclusion treatment (local model) is estimated. The local model can overcome the endogeneity limitation (between EMS response time and survival time) and uncover the potentially spatially and temporally varying correlates of pedestrians' survival time with associated factors to account for unobserved heterogeneity. Moreover, to verify the variations are noticeable, a truncated regression with the two-stage residual inclusion treatment is developed (global model). The modeling results indicate that while capturing the unobserved heterogeneity, the local model outperformed the global model. The empirical results show that EMS response time, speeding, and some pedestrian behaviors are the most important factors that affect pedestrians' survival time in fatal injury crashes. However, the effect of factors on pedestrians' survival time is noticeably varied spatially and temporally. The results and their implications are discussed in detail in the paper.

Using ordered and unordered logistic regressions to investigate risk factors associated with pedestrian crash injury severity in Victoria, Australia

INTRODUCTION

The safety of pedestrians is a major concern in Victoria, Australia. Despite the considerable number of pedestrian fatalities and injuries in traffic crashes, a limited number of studies focused on pedestrian crash severity in Victoria.

METHODS

This study investigates and identifies the influential factors determining the severity of pedestrian injuries in traffic crashes in Victoria by using crash data from 2010 to 2019. An unordered multinomial logit model and an ordered logit model are developed for this purpose.

RESULTS

The results indicate that pedestrian crashes on weekends, in the period of 10 a.m. to 10 p.m., on dark streets, at intersections, in areas with a speed limit above 50 km/h, and on medians or footpaths are associated with a higher probability of severe and fatal injuries. Male pedestrians, children, and older adults (>59) were more likely to sustain a higher level of injury in crashes. Concerning the driver characteristics, no significant relationship was found between pedestrian injury severity and driver gender and license status, but older drivers were more likely to cause severe and fatal injuries. Pedestrian collisions with motorcycles, heavy vehicles, light commercial vehicles, bus/minibus/coach, and trams increase the probability of more severe injuries compared to cars. Moreover, older vehicles are associated with a higher probability of severe pedestrian injuries. Comparison of the model results illustrated that the MNL model was slightly better fitted on the data than the ordered logit model, but the conclusions inferred from these two models were generally similar.

PRACTICAL APPLICATION

To reduce the injuries of pedestrian crashes, we recommend improving lighting conditions and sidewalk design, implementing speed reduction strategies at high pedestrian activity areas, introducing more pedestrian crossings at midblock, installing warning signs to drivers, and discouraging the use of vehicles that are more than 20 years old.

Data linkage for crash outcome assessment: Linking police-reported crashes, emergency response data, and trauma registry records

INTRODUCTION

Traffic crash reports lack detailed information about emergency medical service (EMS) responses, the injuries, and the associated treatments, limiting the ability of safety analysts to account for that information. Integrating data from other sources can enable a better understanding of characteristics of serious crashes and further explain variance in injury outcomes. In this research, an approach is proposed and implemented to link crash data to EMS run data, patient care reports, and trauma registry data.

METHOD

A heuristic framework is developed to match EMS run reports to crashes through time, location, and other indicators present in both datasets. Types of matches between EMS and crashes were classified. To investigate the fidelity of the match approach, a manual review of a sample of data was conducted. A comparative bias analysis was implemented on several key variables.

RESULTS

72.2% of EMS run reports matched to a crash record and 69.3% of trauma registry records matched with a crash record. Females, individuals between 11 and 20 years old, and individuals involved in single vehicle or head on crashes were more likely to be present in linked data sets. Using the linked data sets, relationships between EMS response time and reported injury in the crash report, and between police-reported injury and injury severity score were examined.

CONCLUSION

Linking data from other sources can greatly enhance the information available to address road safety issues, data quality issues, and more. Linking data has the potential to result in biases that must be investigated as they relate to the use-case for the data.

PRACTICAL IMPLICATIONS

This research resulted in a transferable heuristic approach that can be used to link data sets that are commonly collected by agencies across the world. It also provides guidance on how to check the linked data for biases and errors.

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

INTRODUCTION

Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity.

METHOD

Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes.

RESULTS

Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM.

CONCLUSIONS

The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers' working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes.

What affects how far individuals walk?

The number of studies that explore contributing factors that encourage individuals to do more walking trips is proliferated in recent years. However, there is still a lot to know about differentiating between short and long walking trips and their associated influencing factors. The current research investigated the impact of the influencing factors on the share of short and long walking trips across four different trips in 112 traffic analysis zones of Rasht, Iran. The share of walking trips was defined as the proportion of walking trips that originated/ended in an area on all trips that originated/ended in that area. In four trip purposes in short, long, and all trips, the factors associated with the share of walking in origins and destinations were investigated in 24 separate models. The factors included built environmental indices, such as transportation network connectivity and land-use variables, as well as socio-demographic. To differentiate between short and long walking trips, 600 m walking distance was recognized as a proxy. According to the results, the population density was found significantly increase share of walking in both the origins and destinations of short walking trips. Moreover, the models’ goodness of fits were relatively higher in short walking trips comparing long walking trips. This research's findings would give a profound assessment to city planners and decision makers who favor expanding walking as a sustainable mode of transportation.