Fatal Crash Rates Research Articles

Evaluation of the effectiveness of three different interventions on older driver safety over a 12-month period: study protocol for a randomised controlled trial

IntroductionThe growing population of older drivers presents challenges for road safety attributed to age-related declines and increased crash fatality rates. However, enabling older people to maintain their health and independence...

Effects of state opioid prescribing laws on rates of fatal crashes in the USA

BackgroundState opioid prescribing cap laws, mandatory prescription drug monitoring programme query or enrolment laws and pill mill laws have been implemented across US states to curb high-risk opioid prescribing. Previous...

Association of Depression and Antidepressant Use With Driving Behaviors in Older Adults: A LongROAD Study.

Older adults aged 70 and older who drive have higher crash death rates per mile driven compared to middle aged (35-54years) adults who drive in the US. Prior studies have found that depression and or antidepressant medication use in older adults are associated with an increase in the vehicular crash rate. Using data from the prospective multi-site AAA Longitudinal Research on Aging Drivers Study, this analysis examined the independent and interdependent associations of self-reported depression and antidepressant use with driving behaviors that can increase motor vehicle crash risk such as hard braking, speeding, and night-time driving in adults over age 65. Of the 2951 participants, 6.4% reported having depression and 21.9% were on an antidepressant medication. Correcting for age, race, gender, and education level, participants on an antidepressant had increased hard braking events (1.22 [1.10-1.34]) but self-reported depression alone was not associated with changes in driving behaviors.

Exploratory Analysis of Farm Vehicle and Farm Labor Transportation-Related Crashes

ABSTRACT Objective The purpose of this study was to investigate characteristics associated with farm vehicle and farm labor transportation-related roadway crashes in the state of Florida. Agricultural roadway crashes related to these vehicles have not been explored in Florida. Methods The data for this study comes from the Florida Department of Highway Safety and Motor Vehicles (FLHSMV) for the years 2013–2021. The data is recorded by the Florida Highway Patrol when a vehicular crash has occurred. The data is then coded to allow the selection of farm and agricultural-related vehicles. Specifically, this study focuses on farm labor transport vehicles and farm vehicles (e.g. farm tractors). Results There were 744 farm vehicle- and 209 farm labor transportation-related crashes during the 9-year study period. The farm labor transport vehicle crashes involved a total of 420 vehicles and 1,329 individuals, and the farm vehicle crashes involved 1,458 vehicles and 1,652 individuals. Injury risk for non-farm labor transportation vehicle occupants was significantly higher than injury risk for farm labor transportation vehicle occupants. Conclusions The average fatality rate for farm vehicle-involved (2.1 fatalities per 100 crashes) and farm labor transportation vehicle-involved crashes (2.9 fatalities per 100 crashes) were much higher than the overall FL roadway crash fatality rate (0.45 fatalities per 100 crashes).

The value of Association Rule Analysis in understanding serious and fatal road traffic crashes - a case study of the N4 toll road between 2015 and 2019

In spite of the fact that the road traffic crash fatality rate in South Africa is significantly higher than the global average, the characteristics and causes of road traffic crashes are still not well understood. Without a clear understanding of the characteristics and causes, intelligence-led countermeasures to reduce crashes cannot be developed or applied. The analysis of local South African road crash data is of particular importance in identifying the underlying problems responsible for the high crash rates across the country. While descriptive analyses can be used to present a snapshot of the crash problem, they are largely inadequate when it comes to throwing light on the underlying patterns and relationships between the contributing factors and causes of the crashes themselves. This paper presents a study of the use of Association Rule Analysis (ARA) on data related to crashes on the N4 between Pretoria and the Mozambican border, between 2015 and 2019, to see what added value ARA may offer to standard descriptive analyses of crash data. Apriori Association Rule algorithm was employed on the dataset to identify what contributory factors were not initially evident in the occurrence of serious and fatal crashes. ARA revealed that pedestrians, negligent driving, overtaking and single-vehicle crashes are common factors likely to be present in a serious crash. However, this study found that descriptive statistical analysis and ARA methods are not mutually exclusive, but rather complementary. Where descriptive statistical analysis focuses on summarising and describing data, ARA focuses on identifying relationships and patterns within the data.

The effects of Electronic Stability Control (ESC) on fatal crash rates in the United States

Problem: Electronic Stability Control (ESC) is believed to be among the most efficient vehicle safety interventions with reported effects around 50% for fatal single and rollover crashes. However, such estimates have used sample data, which have not controlled for the possibilities of self-selection, behavioral adaptation, increased access to the technology by less safe drivers, and the calculation of effects on very specific categories of crashes. Effects of ESC in the population can therefore be expected to be smaller than is currently believed. Method: National U.S. data for fatal crashes, driving exposure and other control factors, and market penetration of ESC over 1991–2021 were used to calculate whether the trends in fatalities over time in crash rates for singles, rollovers, and fatal crashes in general matched projections from estimates of effectiveness. Results: It was found that downward trends in the relevant crash types were generally present before ESC was introduced, and that the trends thereafter were weaker. Although some trends were consistent with effects of ESC, they were markedly smaller than the projected ones, and could be explained by other factors such as the number of vehicles per capita. At best, the effect for rollovers could be up to two-thirds of previous estimates, no effect was detected for singles, while for all fatal crashes results depended upon the type of analysis performed. These results conflict with conclusions in all published ESC crash sample studies, which have compared vehicles with and without ESC. This discrepancy can be explained by methodological errors in the previous studies using induced exposure methods and self-selected samples. Practical applications: Traffic safety may not be as much improved by technological interventions as believed. Alternative approaches to traffic safety are needed, which do not rely on technology that interferes with driver behavior.

Investigating the severity of single-vehicle truck crashes under different crash types using mixed logit models

Introduction: Almost 90% of fatal road crashes occur in developing countries. Among these countries, Iran has a noticeable fatal crash rate of 21.47 deaths per 100,000 persons. Improving the safety of trucks is of particular importance in Iran where road freight is used to transport almost 90% of the commodities. Researchers have suggested dichotomizing crashes into single- and multi-vehicle categories and found that when this is performed vast differences can be identified between the mechanisms behind these categories of crashes, particularly when investigating truck crashes. Method: This study investigated single-vehicle truck crashes in Khorasan Razavi province in Iran from 2013 to 2021. Likelihood ratio tests were employed to show that separate models are statistically valid for different crash types. Subsequently, three mixed logit crash-type models were developed to investigate 5,703 single-vehicle truck crashes. Results: Four significant variables were exclusive to collisions with an object (brake failure, ABS, primary roads, and rainy or snowy weather), five significant variables were associated with run-off-road crashes (driving a loaded truck, speed limit (>60 km/h), paved shoulders, driving uphill, and inability to control the truck), and three significant variables were associated with overturn crashes (overloaded truck, curved roads, and changing direction suddenly). In all crash types, both fastening the seatbelt and speeding were found to be significant factors. Conclusion: The research highlights the need to analyze single-vehicle truck crashes using distinct crash type models and highlights the unique contributing factors of three common single-vehicle crash types. Practical applications: The study presents recommendations for policy to address key crash risks for trucks in Iran, including education and training to improve driver experience, compliance with seat belt usage, enforcement of speeding, and vehicle technologies to monitor drivers.

A perspective on the epidemiology, hospital care quality, and risk factors for crash mortality in referral trauma centers in northwest Iran

A perspective on the epidemiology, hospital care quality, and risk factors for crash mortality in referral trauma centers in northwest Iran

Investigating transportation safety in disadvantaged communities by integrating crash and Environmental Justice data

Recent efforts to identify disadvantaged communities (DACs) on a census tract level have evoked possibilities of attaining transportation justice and vision zero goals in these areas. To identify DACs, the United States Department of Transportation (USDOT) has developed six comprehensive indicators: economy, environment, equity, health, resilience, and transportation access. The indicators are used to explore the associations between DACs (in 71,728 census tracts) and five years of fatal crashes, providing a comprehensive understanding of safety risks. Specifically, using data on DACs and linking it with census and crash data, this study aims to understand the complex connections between safety (captured through fatal crashes) and disadvantages that communities confront due to a convergence of multiple challenges and burdens using Zero-Hurdle Negative Binomial models. The results reveal that health, resilience, and transportation-disadvantaged tracts are associated with more fatal crashes. The study also found the presence of a higher percentage of the population with bachelor's degrees and increased use of public transportation are correlated with fewer fatal crashes. Also, a higher fatal crash rate is observed in disadvantaged census tracts where a high proportion of the Hawaiian or other Pacific Islander, and American Indian or Alaska Native populations live. This implies that targeted interventions can be explored further in tracts that show high correlations with fatal crashes. The findings contribute to traffic safety by highlighting the risks in DACs, which can help design and implement traffic safety interventions. The insights gained from this study can inform decision-making and help to guide the development of more equitable traffic safety programs in disadvantaged communities.

Trends and characteristics of severe road traffic injuries in children: a nationwide cohort study in Japan.

The purpose of this study was to evaluate temporal trends of characteristics of severe road traffic injuries in children and identify factors associated with mortality using a nationwide database in Japan. We performed a retrospective analysis of Japan Trauma Data Bank (JTDB) from 2004 to 2018. We included patients with traffic injuries under the age of 18 who were hospitalized. The primary outcome was in-hospital mortality. We evaluated trends in characteristics and assessed factors associated with in-hospital mortality using a logistic regression analysis. A total of 4706 patients were analyzed. The most common mechanism of injury was bicycle crash (34.4%), followed by pedestrian (28.3%), and motorcycle crash (21.3%). The overall in-hospital mortality was 11.2%. We found decreasing trends in motorcycle crash and in-hospital mortality and increasing trends in rear passenger seats in cars over the 15-year period. The following factors were associated with in-hospital mortality: car crash (aOR 1.69, 95%CI 1.18-2.40), pedestrian (aOR 1.50, 95%CI 1.13-1.99), motorcycle crash (aOR 1.42, 95%CI 1.03-1.95) [bicycle crash as a reference]; concomitant injuries to head/neck (aOR 5.06, 95%CI 3.81-6.79), thorax (aOR 2.34, 95%CI 1.92-2.87), abdomen (aOR 1.74, 95%CI 1.29-2.33), pelvis/lower-extremity (aOR 1.57, 95%CI 1.23-2.00), spine (aOR 3.01, 95%CI 2.02-4.43); and 5-year increase in time period (aOR 0.80, 95%CI 0.70-0.91). We found decreasing trends in motorcycle crash and in-hospital mortality, increasing trends in rear passenger seats in cars over the 15-year period, and factors associated with in-hospital mortality such as type of mechanisms and concomitant injuries. Strengthening child road safety measures, particularly for rear passenger seats in vehicles, is imperative to enhance our dedication to injury prevention.

Ethnoracial Disparities in Rates of Non-Natural Causes of Death After the 2020 COVID-19 Outbreak in New York State

COVID-19 was associated with increases in non-natural cause mortality in the U.S., including deaths due to drug overdose, homicide, and motor vehicle crashes. Initial reports indicated higher rates of non-natural mortality among ethnoracial minority groups. This report aims to clarify these disparities by documenting trends in non-natural mortality across ethnoracial groups during the 2020 COVID-19 surge in New York State. We report monthly trends in non-natural cause mortality (overall and stratified by ethnoracial status) in New York State from January 2019 through December 2020, which included the COVID-19 onset in March 2020. Total mean monthly unintentional overdose rates per 100,000 increased from 17.45 (before surge: January 2019-February 2020) to 23.19 (after surge: March 2020-December 2020) (mean difference=5.73, 95% CI=3.82, 7.65; p<0.001).Mean monthly homicide death rates increased from 2.34 before surge to 3.55 after surge (mean difference=1.20, 95% CI=0.60, 1.81; p<0.001), with the increase seen primarily in the non-Latinx Black population. Although increasing unintentional overdose death rates before surge equally affected non-Latinx White, Latinx, and non-Latinx Black persons, they remained high for non-Latinx Black persons but dropped for the other 2 groups after the pandemic onset. None of the ethnoracial subgroups showed significant increases in suicide or motor vehicle crash death rates. Non-Latinx Black persons showed disproportionately high and sustained increased rates of unintentional overdose and homicide death rates after the 2020 COVID-19 surge in New York State. Fatality review and death scene investigation research is needed to better understand these disparities.

How the physical inactivity is affected by social-, economic- and physical-environmental factors: an exploratory study using the machine learning approach

ABSTRACT Previous studies have utilized regression models to investigate the impact of environmental factors on physical activity. However, such approaches are inadequate for data-driven analysis seeking to identify robust associations from the intricate and multi-variable interactions between physical activity and environmental factors. With the emergence of the concept of the exposome, which encompasses the totality of exposures, this paper explores machine learning models for predicting the percentage of physical inactivity in U.S. counties, while considering 28 social-, economic-, and physical-environmental factors. The aim of this study is to address the research gap and gain insight into the complex associations between environmental exposures and physical activity. Five machine learning models were tested, and the performances were compared to select the best classifier for further investigation. This study used data from the Behavioral Risk Factor Surveillance System (BRFSS) of the Centers for Disease Control and Prevention. The mean population of all counties was 102,841, and the mean percentage of population below 18 years was 22.3%. The partial dependence plot analysis indicated that only one feature – bachelor’s degree – exhibited a close-to-linear relationship with physical inactivity. Motor-vehicle crash death rate and mean temperature showed nonlinear and non-monotonic relationships with the predicted percentage of physical inactivity.

Effects of emotional intelligence on dangerous driving: a comparison between commercial and non-commercial drivers

ABSTRACT Drivers with a Commercial Driver’s License (CDL) experience higher rates of fatal and serious injury crashes compared to non-commercial drivers. Emotional Intelligence (EI) has shown to be a significant predictor of dangerous driving among non-commercial drivers. However, there is limited research investigating EI within commercial drivers. This paper investigates the relationship between drivers’ EI and dangerous driving behaviors, and how this differs between commercial (N = 595) and non-commercial (N = 615) drivers. A survey was conducted, where EI was measured using Trait Emotional Intelligence Questionnaire, and risky driving using Dula Dangerous Driving Questionnaire. Analyses confirmed (1) commercial drivers have lower EI than non-commercial (ANCOVA, p < .01); (2) commercial drivers are more likely to report more dangerous driving behaviors (MANCOVA, p < .01); and (3) lower EI is associated with increased dangerous driving, and these effects are strongest amongst commercial drivers (linear regression, p < .001). Promoting and improving EI can help prevent dangerous driving and reduce crash risk.

Leveraging probe data to model speeding on urban limited access highway segments: Examining the impact of operational performance, roadway characteristics, and COVID-19 pandemic

Stay-at-home orders - imposed to prevent the spread of COVID-19 - drastically changed the way highways operate. Despite lower traffic volumes during these times, the rate of fatal and serious injury crashes increased significantly across the United States due to increased speeding on roads with less traffic congestion and lower levels of speed enforcement. This paper uses a mixed effect binomial regression model to investigate the impact of stay-at-home orders on odds of speeding on urban limited access highway segments in Maine and Connecticut. This paper also establishes a link between traffic density and the odds of speeding. For this purpose, hourly speed and volume probe data were collected on limited access highway segments for the U.S. states of Maine and Connecticut to estimate the traffic density. The traffic density was then combined with the roadway geometric characteristics, speed limit, as well as dummy variables denoting the time of the week, time of the day, COVID-19 phases (before, during and after stay-at-home order), and the interactions between them. Density, represented in the model as Level of Service, was found to be associated with the odds of speeding, with better levels of service such as A, or B (low density) resulting in the higher odds that drivers would speed. We also found that narrower shoulder width could result in lower odds of speeding. Furthermore, we found that during the stay-at-home order, the odds of speeding by more than 10, 15, and 20 mph increased respectively by 54%, 71% and 85% in Connecticut, and by 15%, 36%, and 65% in Maine during evening peak hours. Additionally, one year after the onset of the pandemic, during evening peak hours, the odds of speeding greater than 10, 15, and 20 mph were still 35%, 29%, and 19% greater in Connecticut and 35% 35% and 20% greater in Maine compared to before pandemic.

Modeling faults among mountainous highway fatal crash-involved drivers with different route familiarity levels

Objective With the constrained topography and road geometry, adverse weather conditions and restricted roadway facilities, mountainous highway crash rates and fatality rates are much higher. Considering the potential influence of driver’s route familiarity level on driving behavior and fault assignment, this research investigates high- and low- route familiarity level drivers (HRF and LRF drivers) fault assignment in mountainous highway fatal crashes in Yunnan Province of China by examining factors of driver, crash/environment and pre-crash behaviors. Methods Yunnan Province is famous for its tourism, and tourism can also bring in many drivers with low-route familiarity levels. Spatial distance away from residence-based method is used for identifying route familiarity levels of the drivers in this study. We employed two separate binary logistic regression models to investigate the effects of the explanatory variables on the likelihoods that the HRF or LRF drivers were at fault in the mountainous highway fatal crashes. Results The results show that driver under alcohol influence, sharp turn, dawn/dusk and left turning are 4 common factors that significantly influence both HRF and LRF drivers’ fault assignments. Factors including driver age, driver seatbelt use, weather condition, road type, section type, lighting condition and pre-crash behaviors have different or opposite influences on HRF and LRF drivers’ fault assignments. HRF drivers are much easier to be distracted under the conditions that are without the need of extra attention. LRF drivers have much more difficulties in figuring out, responding to and making timely driving behavior adjustment to ensure their driving safety on the high-risk sections like tunnels, continuous long downhills and sharp turns. Street parking/backing and left turning of the LRF drivers are very serious problems on the mountainous highways in China. Conclusions There is a large difference of significant factors contributing to the fault assignment of HRF and LRF drivers in mountainous highway fatal crashes. Some more effective and targeted countermeasures are put forward for HRF and LRF drivers and transportation managers to improve mountainous highway traffic safety.

Differences in road safety ‘logics’ between high income countries (HICs) and low income countries (LMICs)

Existing models and frameworks in relation to road safety that had been developed in High Income Countries (HICs) may not be adequate for Low and Middle Income Countries (LMICs). Associations between various indicators were examined, providing further evidence of different ‘logics’ in relation to road safety in HICs and LMICs. Analyses were undertaken by comparing indicators from different sources, such as the WHO and ESRA. From these analyses it has appeared that relationships which hold in HICs do not necessarily hold in LMICs.Examples are given in relation to factors affecting the road crash fatality rate, risky behaviour in traffic, and support for road safety policy measures. Given the importance of the road safety challenge in LMICs, more research is needed to develop models and frameworks that are more appropriate and fit better the road safety context in LMICs.

Association Between State-Level Access to Reproductive Care and Suicide Rates Among Women of Reproductive Age in the United States

Many states in the United States enforce restrictions to reproductive care, with access to abortion remaining a highly divisive issue. Denial of abortion is linked with heightened stress and anxiety among reproductive-aged women. However, no studies have tested whether access to reproductive care is linked to suicide. To evaluate whether state-level restrictions in access to reproductive care in the United States were associated with suicide rates among reproductive-aged women from 1974 to 2016. A longitudinal ecologic study with a difference-in-differences analysis assessed whether annual changes in the enforcement of state-level restrictions to reproductive care were related to annual state-level suicide rates vs rates of death due to motor vehicle crashes. Duration of follow-up varied between different states (range, 4-40 years), contingent on the first year that restrictions were implemented. Models controlled for year and state fixed effects and other relevant demographic and economic factors. Analyses were conducted between December 2021 and January 2022. Targeted Regulation of Abortion Providers (TRAP) laws index measuring state-year-level restrictions to reproductive care. Annual state-level suicide rates and motor vehicle crash death rates among reproductive-aged women (ages 20-34 years; target group) vs women of postreproductive age (ages 45-64 years; control group). Twenty-one US states enforced at least 1 TRAP law between 1974 and 2016. Annual rates of death by suicide ranged from 1.4 to 25.6 per 100 000 women of reproductive age to 2.7 to 33.2 per 100 000 women of postreproductive age during the study period (1974-2016). Annual motor vehicle crash death rates among women of reproductive age ranged from 2.4 to 42.9 per 100 000. Enforcement of TRAP laws was associated with higher suicide rates among reproductive-aged women (β = 0.17; 95% CI, 0.03 to 0.32; P = .02) but not women of postreproductive age (β = 0.06; 95% CI, -0.11 to 0.24; P = .47) nor to deaths due to motor vehicle crashes (β = 0.03, 95% CI, -0.04 to 0.11; P = .36). Among reproductive-aged women, the weighted average annual-state level suicide death rate when no TRAP laws were enforced was 5.5 per 100 000. Enforcement of a TRAP law was associated with a 5.81% higher annual rate of suicide than in pre-enforcement years. Findings remained significant when using alternative, broader indices of reproductive care access and different age categorizations. In this study with a difference-in-differences analysis of US women, restrictions on access to reproductive care from 1974 to 2016 were associated with suicide rates among reproductive-aged women. Given the limitations of the ecologic design of this study, further research is needed to assess whether current factors affecting access to reproductive care services are related to suicide risk among women of reproductive age and to inform suicide prevention strategies.

Road environment characteristics and fatal crash injury during the rush and non-rush hour periods in the U.S: Model testing and cluster analysis

This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day – rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.

Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia

Emergency medical services (EMS) is a critical component high-quality health-care. Road traffic crashes are one of the major causes of death and injury around the world. In the Kingdom of Saudi Arabia (KSA), the road traffic crash death rate has reached 29 per 100,000 people, or 11.25% of total deaths in 2018. The Saudi Red Crescent Authority (SRCA) is responsible for providing EMS in KSA. However, its average response time to road traffic crashes (14 min) is slower than the international standard (8 min). In this study, we analyzed a total of 57,928 road traffic crash reports recorded in 2019 by the SRCA for all cities in the KSA. The effective planning of SRCA, which is highly dependent on historical data analysis, is critical for decreasing emergency response times and, thereby, rescue times. Thus, we aimed to analyze response and rescue time trends related to road traffic crashes to identify significant factors that would assist decision-makers in enhancing the responsiveness of EMS through rescheduling, relocating stations, managing resources (e.g., ambulances and staff), or strategies based on the factors identified as significant. Statistical tools were used to identify the significant factors that affect the SRCA’s performance in responding to road traffic crashes. The analysis results showed that the response and rescue times for road traffic crashes of the SRCA varied within as a result of different factors like geographical region, academic calendar, crash time, weather conditions, the severity of injuries sustained, and the number of injured people. The central regions have the longest average response and rescue times while the northern regions have the shortest ones. Response and rescue times were higher during daytime hours and holidays than during night hours and non-holiday periods. The results also indicated that EMS response and rescue times were significantly associated with mortality rate for road traffic crashes. As a result, by decreasing response and rescue times, the mortality rate for traffic accidents in the KSA could be decreased. Identifying and understanding significant factors that impact EMS response and rescue times can help decision-makers improve the performance of EMS, reduce response and rescue times, save people’s lives, and reduce governmental healthcare costs.

Crash characteristics for classic/historic vehicles and comparisons to newer vehicles

Introduction: Older vehicles, commonly referred to as “classic,” “vintage,” or “historic” vehicles (CVH), share the roadways with newer vehicles. Older vehicles lacking safety systems likely come with an increased risk of fatality, however there is no study examining the typical conditions for crashes involving CVH. Method: This study utilized information from crashes occurring in 2012 to 2019 to estimate fatal crash rates for vehicles grouped by model year deciles. Data from crashes documented in the National Highway Traffic Safety Administration’s (NHTSA) FARS and GES/CRSS data sets were utilized to examine roadway, temporal, and crash types for passenger vehicles produced in 1970 or earlier (CVH). Results: These data show CVH crashes are rare (<1% of crashes), but carry a relative risk of fatality from 6.70 (95th CI: 5.44–8.26) for impacts with other vehicles, which was the most common crash, to 9.53 (7.28–12.47) for rollovers. Most crashes occurred in dry weather, typically during summer, in rural areas, most frequently on two lane roads, and in areas with speed limits between 30 and 55 mph. Factors associated with fatality for occupants in CVH included alcohol use, lack of seat belt use, and older age. Conclusions and Practical Applications: Crashes involving a CVH are a rare event but have catastrophic consequences when they do occur. Regulations that limit driving to daylight hours may lower the risk of crash involvement, and safety messaging to promote belt use and sober driving may also help. Additionally, as new “smart” vehicles are developed, engineers should keep in mind that older vehicles remain on the roadway. New driving technologies will need to safely interact with these older, less safe vehicles.