Profiles of pregnant women encountering motor vehicle crashes in Taiwan, 2008-2017

BACKGROUND

Understanding demographic profiles is essential to the assessment of health burden imposed by motor vehicle crashes (MVCs) on pregnant women. However, Asian studies that have examined it are lacking. The study aimed to describe the demographic characteristics and prevalence of MVCs involving pregnant women in Taiwan.

METHODS

A cross-sectional study conducted by the Taiwan Birth Notification dataset from 2008 to 2017 was linked with the police-reported traffic collision registry to identify pregnant women involved in MVCs. The pregnant women were categorized according to their gestational age, age at delivery, the role of road user (driver, passenger, or pedestrian), and vehicle types (car, two-wheeled motor vehicle, or others). A chi-square test was performed for the significance test.

RESULTS

A total of 22,134 (1.13%) pregnant women were involved in MVCs in the study period. Two-wheeled motor vehicle (47.9%) and driver (81.4%) were the mainly reported vehicle type and road user at the crash scenes, respectively. The majority of MVCs occurred in pregnant women aged 28-34 years. The number of MVCs rapidly declined after 37 weeks of gestation, especially two-wheeled motor vehicle or car crashes. However, the number of pedestrian victims climbed up during the third trimester.

CONCLUSION

Pregnant women are susceptible to MVCs regardless of their gestational age, role of a road user, or type of vehicle. The findings of this study emphasize the need for increased awareness of traffic collision prevention among pregnant women aged 28-34. In addition, improving pedestrian safety is essential for the reduction of pregnant victims.

What is counted counts: An innovative linkage of police, hospital, and spatial data for transportation injury prevention

INTRODUCTION

Growing research indicates transportation injury surveillance using police collision reporting alone underrepresents injury to vulnerable groups, including pedestrians, cyclists, and people of color. This reflects differing reporting patterns and non-clinicians' challenge in accurately evaluating injury severity. To our knowledge, San Francisco is the first U.S. city to link and map hospital and police injury data. Analysis of linked data injury patterns informs interventions supporting traffic fatality and injury prevention goals.

METHODS

Injury and fatality records 2013-2015 were collected from San Francisco Police, Emergency Medical Services (EMS), Medical Examiner, and Zuckerberg San Francisco General Hospital (ZSFG). Probabilistic linkage was conducted using LinkSolv9.0 on match variables collision/admission time, name, birthdate, sex, travel mode, and geographic collision location.

RESULTS

From 2013-2015, this study identified 17,000+ transportation-related injuries on public roadways in San Francisco. Twenty-six percent (n = 4,415) appeared in both police and ZSFG sources. Linked injury records represent 39% of police records (N = 11,403) and 43% of hospital records (N = 10,223). Among hospital records, 34% of cyclist, 38% of motor vehicle occupant, 61% of pedestrian, and 54% of motorcyclist records linked with a police record. Linkage rate varied by travel mode even after controlling for injury severity. Transportation-injured ZSFG-treated patients lacking police reports were more often cyclists, male, Hispanic or Black, and less often occupants of motor vehicles compared to those with injuries captured only in police reports.

CONCLUSIONS

Incorporating hospital and EMS spatial data into injury surveillance systems historically reliant on police reports offers trifold benefits. First, linkage captures injuries absent in police data, adding data on populations empirically vulnerable to injury. Second, it improves injury severity assessment. Finally, linked data better informs and targets interventions serving injury-burdened populations and road users, advancing transportation injury prevention.

PRACTICAL APPLICATIONS

Linkage closes data gaps, improving ability to quantify injury and develop evidence-based interventions for vulnerable groups.

A pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles

This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. Specifically, the vehicle type is predicted on the basis of traumatic injury suffered by casualties, exploiting machine learning algorithms. In the present study, AI-assisted diagnosis was shown to have correct prediction about 70% of the time. In pedestrians struck by trucks, more severe injuries were appreciated in the facial skeleton, lungs, major airways, liver, and spleen as well as in the sternum/clavicle/rib complex, whereas the lower extremities were more affected by fractures in pedestrians struck by cars. Although the distinction of the striking vehicle should develop beyond autopsy evidence alone, the presented approach which is novel in the realm of forensic science, is shown to be effective in building automated decision support systems. Outcomes from this system can provide valuable information after the execution of autoptic examinations supporting the forensic investigation. Preliminary results from the application of machine learning algorithms with real-world datasets seem to highlight the efficacy of the proposed approach, which could be used for further studies concerning this topic.

Evaluation of the sequential organ failure assessment score and newly introduced criteria - Traumasis - in traffic collision patients

BACKGROUND

The aim of this study was to evaluate the performance of the Sequential Organ Failure Assessment (SOFA) score and the newly introduced criteria, traumasis, defined as a SOFA score 2 or more among trauma patients.

METHODS

Consecutive adult traffic collision patients who were admitted to the study hospital emergency department (ED) from January 2017 to December 2018 were enrolled retrospectively in the study. The primary outcome was in-hospital death. The SOFA score was calculated using relevant initial ED data. Traditional risk scores for trauma patients, such as the injury severity score (ISS), the revised trauma score (RTS), and the trauma injury severity score (TRISS), were also calculated.

RESULTS

A total of 927 patients were available for analysis, of whom 46 died (5.0%). The median SOFA score was 1.0 (interquartile range [IQR], 0.0-3.0). A total of 417 patients (45.0%) were identified as having traumasis (SOFA score ≥ 2), of whom 44 died (10.6%). The area under the receiver operating characteristic (AUROC) curve of the SOFA score (0.91; 95% confidence interval [CI] 0.87-0.95) was comparable with that of the TRISS (0.88; 95% CI, 0.84-0.93) and better than that of the ISS(0.81; 95% CI 0.75-0.86) and the RTS (0.82; 95% CI 0.75-0.90). The sensitivity, specificity, positive predictive value and negative predictive value of the traumasis criteria for the primary outcome were 95.7%, 63.0%, 11.9%, and 99.6%, respectively. The net reclassification improvement for the comparison between the traumasis criteria and major trauma criteria (ISS ≥ 15) was 0.69 (95% CI, 0.55-0.82; p < 0.001). The multivariate Cox regression model showed that the SOFA score (adjusted hazard ratio [aHR] 1.52; 95% CI 1.37-1.67) and traumasis (aHR 11.40; 95% CI 2.70-48.13), respectively, was independently associated with higher in-hospital mortality.

CONCLUSION

The SOFA score can be used as a reliable tool for predicting in-hospital death among traffic collision patients. The newly introduced criteria, traumasis, may be used as a risk-stratification and quality-control criteria among patients with trauma, similar to the sepsis criteria among patients with infectious disease.

Prediction model for autopsy diagnosis of driver and front passenger in fatal road traffic collisions

Road traffic collisions (RTC) analysis is almost a daily activity in many autopsy room. Especially when analyzing an RTC with multiple occupants in the car, it can be necessary to distinguish driver from front seat passenger in order to provide the judicial authority with elements useful to understand and to prove who was driving, considering the criminal and civil responsabilities that may derive from it. Despite this, it is beyond doubt that there is enormous difficulty in providing such information. The aim of this paper is then to evaluate whether it is possible to differentiate driver and front seat passenger in case of fatal collisions using a mathematical model based on injury pattern alone. Autopsy reports concerning 90 drivers and 60 front-seat passengers were analyzed. Statistical analysis was used to detect injuries capable of discriminating between driver and passenger, considering skin, skeletal and visceral injuries. Results show that certain skin injuries, fractures and internal organ lesions are possibly associated with drivers and front seat passenger status and the overall injury pattern seems to be able to provide useful information. A mathematical model is presented. The process to distinguishing driver from front seat passenger following fatal motor vehicle collision may use multiple sources of information, including autopsy injury pattern analysis.

The transportation safety of elderly pedestrians: Modeling contributing factors to elderly pedestrian collisions

For the elderly, walking is an important, reliable mobility option, since the elderly frequently lose their physical and/or sensory ability to drive as their age increases. However, elderly pedestrians are vulnerable on the streets and are at great risk of injury or death, when involved in a collision. This is due to not only increased frailty but also such issues as reaction speed and confidence on the streets. Therefore, pedestrian safety for older adults is a growing concern. This paper comprehensively examines the relationship between physical conditions and elderly pedestrian safety at the intersection level. By constructing a multinomial logistic regression (MLR) model, this paper identifies the exclusive contributing factors to elderly pedestrian collisions rather than younger pedestrian collisions. The outputs from the model suggest that facilities such as raised median, three-way intersection, street tree, and park and recreational land use improve the safety of elderly pedestrians. They also imply that bus stops increase elderly pedestrian collisions, while the intersections with crosswalks or colored crosswalks do not contribute to elderly pedestrians' safety, but the safety of younger pedestrians. The findings of this paper provide insight to transportation policies like Complete Street and Vision Zero and help to improve the current road system that are designed for automobiles and young, healthy road users.

Novelty helmet use and motorcycle rider fatality

OBJECTIVES

To compare the risk of fatal injury across helmet types among collision-involved motorcyclists.

METHODS

We used data from a cohort of motorcyclists involved in police-reported traffic collisions. Eighty-four law enforcement agencies in California collected detailed information on helmet and rider characteristics during collision investigations in June 2012 through July 2013. Multiply-adjusted risk ratios were estimated with log-binomial regression.

RESULTS

The adjusted fatal injury risk ratio for novelty helmets was 1.95 (95% CI 1.11-3.40, p 0.019), comparing novelty helmets with full-face helmets. The risk ratios for modular, open-face, and half-helmets, compared with full-face helmets, were not significant.

CONCLUSIONS

A more complete understanding of the inadequacy of novelty helmets can be used in educational and law enforcement countermeasures to improve helmet use among motorcycling populations in California and other US states. Law enforcement approaches to mitigating novelty helmet use would seem attractive given that novelty helmets can be visually identified by law enforcement officers with sufficient training.

Estimating the harms and costs of cannabis-attributable collisions in the Canadian provinces

INTRODUCTION

In 2012, 10% of Canadians used cannabis and just under half of those who use cannabis were estimated to have driven under the influence of cannabis. Substantial evidence has accumulated to indicate that driving after cannabis use increases collision risk significantly; however, little is known about the extent and costs associated with cannabis-related traffic collisions. This study quantifies the costs of cannabis-related traffic collisions in the Canadian provinces.

METHODS

Province and age specific cannabis-attributable fractions (CAFs) were calculated for traffic collisions of varying severity. The CAFs were applied to traffic collision data in order to estimate the total number of persons involved in cannabis-attributable fatal, injury and property damage only collisions. Social cost values, based on willingness-to-pay and direct costs, were applied to estimate the costs associated with cannabis-related traffic collisions. The 95% confidence intervals were calculated using Monte Carlo methodology.

RESULTS

Cannabis-attributable traffic collisions were estimated to have caused 75 deaths (95% CI: 0-213), 4407 injuries (95% CI: 20-11,549) and 7794 people (95% CI: 3107-13,086) were involved in property damage only collisions in Canada in 2012, totalling $1,094,972,062 (95% CI: 37,069,392-2,934,108,175) with costs being highest among younger people.

DISCUSSION

The cannabis-attributable driving harms and costs are substantial. The harm and cost of cannabis-related collisions is an important factor to consider as Canada looks to legalize and regulate the sale of cannabis. This analysis provides evidence to help inform Canadian policy to reduce the human and economic costs of drug-impaired driving.

Sensitivity analysis for matched pair analysis of binary data: From worst case to average case analysis

In matched observational studies where treatment assignment is not randomized, sensitivity analysis helps investigators determine how sensitive their estimated treatment effect is to some unmeasured confounder. The standard approach calibrates the sensitivity analysis according to the worst case bias in a pair. This approach will result in a conservative sensitivity analysis if the worst case bias does not hold in every pair. In this paper, we show that for binary data, the standard approach can be calibrated in terms of the average bias in a pair rather than worst case bias. When the worst case bias and average bias differ, the average bias interpretation results in a less conservative sensitivity analysis and more power. In many studies, the average case calibration may also carry a more natural interpretation than the worst case calibration and may also allow researchers to incorporate additional data to establish an empirical basis with which to calibrate a sensitivity analysis. We illustrate this with a study of the effects of cellphone use on the incidence of automobile accidents. Finally, we extend the average case calibration to the sensitivity analysis of confidence intervals for attributable effects.

Traumatic brain injury in pedestrian-vehicle collisions: Convexity and suitability of some functionals used as injury metrics

BACKGROUND AND OBJECTIVE

Abrupt accelerations or decelerations can cause large strain in brain tissues and, consequently, different forms of Traumatic Brain Injury (TBI). In order to predict the effect of the accelerations on the soft tissues of the brain, many different injury metrics have been proposed (typically, an injury metric is a real valued functional of the accelerations). The objective of this article is to make a formal and empirical comparison, in order to identify general criteria for reasonable injury metrics, and propose a general guideline to avoid ill-proposed injury metrics.

METHODS

A medium-sized sample of vehicle-pedestrian collisions, from Post Mortem Human Subject (PMHS) tests, is analyzed. A statistical study has been conducted in order to determine the discriminant power of the usual metrics. We use Principal Component Analysis to reduce dimensionality and to check consistency among the different metrics. In addition, this article compares the mathematical properties of some of these functionals, trying to identify the desirable properties that any of those functionals needs to fulfill in order to be useful for optimization.

RESULTS

We have found a pair-wise consistency of all the currently used metrics (any two injury metrics are always positively related). In addition, we observed that two independent principal factors explain about 72.5% of the observed variance among all collision tests. This is remarkable because it indicates that despite high number of different injury metrics, a reduced number of variables can explain the results of all these metrics. With regard to the formal properties, we found that essentially all injury mechanisms can be accounted by means of scalable, differentiable and convex functionals (we propose to call minimization suitable injury metric any metric having these three formal properties). In addition three useful functionals, usable as injury metrics, are identified on the basis of the empirical comparisons.

CONCLUSIONS

The commonly used metrics are highly consistent, but also highly redundant. Formal minimal conditions of a reasonable injury metric has been identified. Future proposals of injury metrics can benefit from the results of this study.

Denoising traffic collision data using ensemble empirical mode decomposition (EEMD) and its application for constructing continuous risk profile (CRP)

Filtering out the noise in traffic collision data is essential in reducing false positive rates (i.e., requiring safety investigation of sites where it is not needed) and can assist government agencies in better allocating limited resources. Previous studies have demonstrated that denoising traffic collision data is possible when there exists a true known high collision concentration location (HCCL) list to calibrate the parameters of a denoising method. However, such a list is often not readily available in practice. To this end, the present study introduces an innovative approach for denoising traffic collision data using the Ensemble Empirical Mode Decomposition (EEMD) method which is widely used for analyzing nonlinear and nonstationary data. The present study describes how to transform the traffic collision data before the data can be decomposed using the EEMD method to obtain set of Intrinsic Mode Functions (IMFs) and residue. The attributes of the IMFs were then carefully examined to denoise the data and to construct Continuous Risk Profiles (CRPs). The findings from comparing the resulting CRP profiles with CRPs in which the noise was filtered out with two different empirically calibrated weighted moving window lengths are also documented, and the results and recommendations for future research are discussed.

Analysis of child-vehicle collision injuries by vehicle type

PURPOSE

This study aimed to determine the severity of injuries to each body region of child pedestrians struck by different types of vehicles.

METHODS

We collected in-depth data from the Institute for Traffic Accident Research and Data Analysis on pedestrians younger than 13 years with any bodily injuries from collisions with vehicles between 1993 and 2004.

RESULTS

Sixty-eight patients with a mean age of 6.9 ± 2.4 years were included in the study. In collisions, vehicles caused higher Abbreviated Injury Scale (AIS) scores than those from impact with the road. Injury Severity Score and AIS values were higher with one-box or sports utility vehicles compared with those in sedan vehicles, but the differences were not statistically significant. The mean AIS score of head injuries was significantly higher with one-box or sports utility vehicles than that with sedans (1.6 ± 2.1 vs 0.5 ± 1.1, P < .05). The mean AIS score of the lower extremities was significantly higher with sedans than that with one-box or sports utility vehicles (1.2 ± 1.0 vs 0.5 ± 0.9, P < .05).

CONCLUSIONS

The type and severity of injuries in child-car collisions vary by type of vehicle and pedestrian kinematics.