Police-reported Crash Data Research Articles

Injury Assessment of Pedestrians, Bicyclists, and Motorcyclists Utilizing Linkage of Police-Reported Crash Data to EMS Data in Michigan

In Michigan, police crash reports are the primary source of data on traffic crashes. Though many aspects are very reliable, some details are difficult to observe or require subjective judgement. Injury severity, which is central to the road safety issue, unfortunately falls into the second category. The KABCO injury scale used by law enforcement classifies injuries as “possible,” “suspected minor,” and “suspected serious” which may not correspond to determinations made by medical professionals. EMS records can provide a means to improve crash injury data using medical evaluations, but these records are not linked to the police reports. This project explores the linkage of police crash reports and EMS records using both Michigan data sources from 2018 to 2021. Given the association between severity of injuries and person type, three crash subsets with high injury risk were the focus: pedestrians, bicyclists, and motorcyclists. Collectively, these person types represent 25.1% of the police reported fatalities and suspected serious injuries in Michigan in 2022 despite being only 1.2% of the crashing population. Person-level records for pedestrians, bicyclists, and motorcyclists were identified and divided into three separate datasets for each data source. EMS data events were restricted cases believed to be associated with traffic crashes using ICD-10 injury codes. Personal identifiers were not provided, so probabilistic linkage was used to link person crash records to EMS records. Event date, location, ambulance ID, and person demographic variables were utilized. Linkage rates and injury severity agreement was computed to assess differences between the two datasets.

The role of human errors and violations in pedestrian-related crashes: Harnessing a unique database and accounting for heterogeneity

IntroductionHuman factors are often major contributors to pedestrian crashes. However, police-reported pedestrian-involved crash data often have gaps in crash details. Overcoming this limitation, the Pedestrian and Bicycle Crash Data Tool (PBCAT) provides a more comprehensive high-quality database capturing the sequence of events. MethodsIn addition to human and roadway environmental factors, there could be unobserved factors (e.g., pedestrian conspicuity, impact speed, or riskiness by a driver) that could be either unavailable or not used in the analysis; however, these unobserved factors could have a significant influence on pedestrian injuries. This study applies finite mixture models to address unobserved heterogeneity in pedestrian injuries which is usually overlooked. As a result, the associations of one or more of the observed factors with pedestrian injuries across different latent (unobserved) classes can be different. ResultsHarnessing this unique database for North Carolina reveals that in most (95%) of the crashes (N=24,886) occurring between 2009 and 2019, pedestrians were either killed or injured. Risky behaviors by drivers and pedestrians contributed to 7.91% and 50.59% of these crashes, respectively. Recognition errors (e.g., dash or dart-out) and violations (e.g., failure to yield) by pedestrians contributed to 22.08% and 28.58% of crashes, respectively. Recognition errors and violations by drivers contributed to only 2.12% and 3.11% of crashes respectively each of which is significantly lower than those by pedestrians. Results of the ordered Probit model indicate that the chance of pedestrian fatality is significantly higher if a pedestrian makes recognition errors and violations, a driver makes performance errors, and either the pedestrian or driver is impaired. Conclusions and practical implicationsThe finite mixture model shows that pedestrians belong to two latent groups across which there is significant heterogeneity in pedestrian injuries and variations in the associations of observed factors with pedestrian injuries. The practical implications are discussed in the paper.

Schizophrenia, antipsychotic treatment adherence and driver responsibility for motor vehicle crash: a population-based retrospective study in British Columbia, Canada

ObjectiveTo examine the relationship between schizophrenia, antipsychotic medication adherence and driver responsibility for motor vehicle crash.DesignRetrospective observational cohort study using 20 years of population-based administrative health and driving data.SettingBritish Columbia,...

Benchmarks for retrospective automated driving system crash rate analysis using police-reported crash data

Objectives With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the U.S., we are now approaching an inflection point in the history of vehicle safety assessment. The process of retrospectively evaluating ADS safety impact (as seen with seatbelts, airbags, electronic stability control, etc.) can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a “benchmark” crash rate. Most benchmarks generated to-date have focused on the current human-driven fleet, which enable researchers to understand the impact of the introduced ADS technology on the current crash record status quo. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. Methods: All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Results Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identified several important crash rate dependencies (geographic region, road type, and vehicle type), and show how failing to account for these features in ADS comparisons can bias results. Conclusions Working with police-reported crash data to create crash rate benchmarks is fraught with challenges. Researchers should be cautious in their selection of crash rate benchmarks. We present these challenges, discuss their consequences, and provide analytical guidance for addressing them. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks.

Exploring master scenarios for autonomous driving tests from police-reported historical crash data using an adaptive search sampling framework

Crash scenario-based testing is crucial for assessing autonomous driving safety. However, existing studies on scenario generation tend to prioritize concrete scenarios for direct testing, neglecting the construction of fundamentally functional scenarios with a broader range. Police-reported historical crash data is a valuable supplement, yet detecting all potential crash scenarios is laborious. In order to address this issue, this study proposes an adaptive search sampling framework based on deep generative model and surrogate model (SM) to extract master scenario samples from police-reported historical crash data. The framework starts with selecting representative samples from the full crash dataset as initial master scenario samples using various sampling techniques. Evaluation indexes are then constructed, and derived scenario samples are synthesized using the deep generative model. To enhance efficiency, an SM is established to replace the generative model’s training and data generation process. Based on the SM, an adaptive search sampling method is developed, which iteratively adjusts the sampling strategy using the Similarity Score to achieve comprehensive sampling. Experimental results demonstrate the notable advantage of the adaptive search sampling method over other sampling methods. Furthermore, statistical analysis and visualization assessments confirm the effectiveness and accuracy of the proposed method.

Comparative analysis of injury identification using KABCO and ISS in linked North Carolina trauma registry and crash data

Objective The purpose of this study was to examine differences between police-reported injury severity and trauma registry data among persons with linked records in North Carolina and quantify the degree of alignment. Methods We analyzed linked North Carolina trauma registry and motor vehicle crash data from 2018. Injury severity identification was assessed using police-reported 5-point scale KABCO from crash data and Injury Severity Score (ISS) from trauma records. The analysis was stratified by age, sex/gender, race, ethnicity, and road users type to examine differences across groups. We calculated sensitivity, specificity, positive predictive values, and negative predictive values between police-reported injury severity and trauma registry data using ISS as the gold standard. Results A higher proportion of patients were classified as suspected minor injuries (39.0%) compared to moderate injuries in trauma registry (25.1%). Police-reported crash data underreported injury severity when compared to trauma registry data. Police-reported KABCO had a higher degree of specificity when classifying minor injuries (79.3%) but substantially underestimated seriously injured patients, with a sensitivity of 49.9%. These findings were also consistent when stratified by subpopulations. Conclusion Hospital-based motor vehicle crash data are a main source of injury severity identification for road safety. Police-reported data were relatively accurate for minor injuries but not seriously injured patients. Understanding the characteristics of each data source both separately and linked will be critical for problem identification and program development to move toward a safe transportation system for all road users.

Systemic Predictive Safety Analysis of Pedestrian Crashes for Montgomery County’s Vision Zero Program

The goal of Vision Zero is the prevention of all traffic fatalities and serious injuries. Although traditional transportation planning is reactive to locations where serious crashes occur, some agencies are taking a more proactive approach to safety to improve locations with high expected crashes before someone is seriously injured or killed. This paper presents the results of a systemic safety analysis that produced two pedestrian-related safety performance functions for Montgomery County, MD, including 1) motor vehicle crashes with pedestrians at intersections at night and 2) through-movement motor vehicle crashes with pedestrians at road sections. These models were built using negative binomial regression of police-reported crash data collected from 2015 to 2019 for most of the county road network integrated with land use-, demographic-, and roadway variables collected by the Montgomery County Planning Department for 16,387 intersections (stop-controlled and signalized) and 29,715 segments (all functional classifications except freeways). Both models identified key transportation-related exposure variables, including motor vehicle and pedestrian volumes, proximity to transit, and crosswalk locations; they also presented land use contexts that may explain where pedestrians are likely to walk and be exposed to crash risks. These results build on current systemic safety literature and demonstrate the data collection and analysis methods that can be used in a county-level Vision Zero context to improve safety for all who walk. This paper summarizes the analysis approach, including exposure modeling, crash modeling, and applications for identifying both high-risk locations and potential mitigations. Considerations for equity and long-term planning are also discussed.

Examining the Relationship between Connected Vehicle Driving Event Data and Police-Reported Traffic Crash Data at the Segment- and Event Level

Police-reported crash data have been the de facto element used by the transportation agencies in developing and implementing traffic safety projects. This approach is reactive in nature and can lead to suboptimal investment decisions owing to inherent challenges in crash data analysis. Because of their large-scale and near real-time availability, connected vehicle (CV) driving event data have emerged as a promising means of addressing these challenges. This study utilized CV event data for three different event types, namely, acceleration, braking, and cornering at three severity levels (easy, normal, and harsh), to examine the viability of using these data in traffic safety analysis. The results showed a strong correlation between crash frequency and CV driving event frequency. CV event data also improved the goodness-of-fit of crash frequency models. The results also showed that the relationship between CV driving events and traffic volume and roadway geometric data were generally consistent with the trends that crash data usually exhibit with the same predictors. This was true at both segment level and individual event level, as well as when the data were examined across different event/crash types. Overall, the results showed a strong case for these data to be used in traffic safety analyses as a complement to, or in lieu of, crash data.

Injury severity of single-vehicle weather-related crashes on two-lane highways

Roadway safety is immensely affected during adverse weather conditions. Therefore, this study seeks to quantify such effects on the injury severity of involved drivers in single-vehicle crashes on two-way highways. This should be done by identifying risk factors and accommodating the heterogeneous impacts of these factors by accounting for the heterogeneity in means and variances of random parameters in the estimated model using seven years of police-reported crash data from 2010 to 2016 in Oregon. The study findings confirm the superior performance of the mixed logit model with heterogeneity in the means and variances in terms of statistical fit comparable to the traditional mixed logit model and mixed logit model with heterogeneity in the means only. The estimation results reveal that some factors such as female drivers, fatigued drivers, crashes in the early morning (between 4:00 am and 8:00 am), and crashes that occurred in darkness with no streetlights were found to increase serious injury outcomes. The empirical findings can offer evidence-based insights to help in decision-making and countermeasure selection that is aimed at improving safety under adverse weather conditions.

Effect of helmet-wearing regulation on electric-bike riders: a case study of two cities in China

Abstract Objectives Electric bikes (e-bikes) are widely used for commuting and delivery in China. With the rapid increase in e-bikes on the road, related accidents have become crucial issues threatening the public. This research aims to explore the protective effect of helmet-wearing regulation and to investigate some factors influencing head injury with reference to two case cities in China, obtaining information to protect e-bike riders. Methods The traffic police-reported crash data cover the periods before and after the implementation of helmet-wearing regulations in Taizhou (with data from 2017 to 2019) and Nanning (with data in 2020) of China. Preliminary statistical analysis, logistic regression and chi-square test with a Bonferroni correction were applied in the research. Results A lack of helmets was common among victims in the context of a high helmet-wearing rate among general e-bike riders in Taizhou, indicating that fatality could be avoided to some extent by wearing helmets. Specifically, helmet wearing could reduce the probability of suffering fatal head injury by 6.4%. After the regulation implementation in Taizhou, the proportion of fatal head injuries decreased from 89% to 79%, remaining at a high level, which indicates that other measures in addition to helmet-wearing regulations should be taken. According to the results from Nanning, mandatory regulation worked more effectively than encouraged regulation in terms of reducing head injury. Conclusions Mandatory helmet-wearing regulations are highly recommended to policymakers to reduce head injury among e-bike riders. Measures in addition to helmet-wearing regulation, e.g. enhancing law-abiding awareness and improving road infrastructure, should be considered to further protect e-bike riders.

Examining factors influencing the severity of motorized two-wheeler crashes in Delhi

Failure to meet road safety targets has necessitated urgent actions from stakeholders worldwide, especially in developing countries like India. Road safety of motorized two-wheelers (MTWs), one of India’s most preferred travel modes for urban commutes, is in danger and witnessing threatening figures of fatalities and injuries. Most of the studies in the domain of MTW safety were conducted in developed countries, with very limited research in countries having a significant proportion of MTWs. The present work investigates police-reported crash data to identify the contributory factors of motorized two-wheeler crash severity. Data from MTW crash-prone areas were selected from Delhi, which is leading in road traffic fatalities among the million-plus urban cities in India. A binary logistic regression model was developed using the data for 2016–2018 period. The model results show that the odds of fatal motorized two-wheeler crashes increase when the following circumstances apply: crash occurs on underpasses; involves bus, truck, heavy motor vehicle (lorry, crane) as the striking vehicle; when hit-and-run type of crash occurs and when older age-group (> = 55) riders are involved. Finally, based on the findings, countermeasures were suggested to facilitate policymakers and traffic enforcement agencies, in improving the road safety situation of MTW users.

Insights Into Wire Rope Safety Barrier Crashes Based on Police-Reported Statistics and Narratives

Wire rope safety barrier (WRSB) is known to be highly effective in reducing the severity of off-road or off-path crashes, however fatal and serious injury outcomes still occur in a small percentage of WRSB-involved crashes. Yet, there is little research on why some WRSB crashes result in severe injury while the majority of crashes involve minor injury outcomes. This paper includes detailed analysis of off-path crashes involving WRSB to better understand associated factors that contribute to severe injury outcomes. Three datasets were analysed: four years of NSW police-reported crash data (2014-2017); ten years of Victorian police-reported crash data and police narratives (2008-2017); five years of police narratives from the US (2001-2005). Results supported earlier research that indicated a relatively low percentage of WRSB crashes involved fatal and serious injury outcomes. Yet, adverse vehicle dynamics were also observed, such as vehicle ricochetting and barrier override, suggesting barrier safety performance could be improved. Some crashes also involved impact angles greater than those used in crash tests, supporting earlier, more detailed research that the full range of real-world crash conditions involving WRSB are not covered in barrier test protocols, and these might be the conditions contributing to severe injury outcomes. The findings provide insight into the potential factors associated with the serious injury outcomes of crashes and can guide further improvements in this effective highway crash countermeasure. Future research should include detailed analysis of real-world crashes to identify factors contributing to adverse crash dynamics, injury outcomes and subsequent implications for barrier design.

Evaluation of intersection crashes using naturalistic driving data through the lens of future I-ADAS

Objective Intersection advanced driver assistance systems (I-ADAS) with the capability to detect possible collisions and perform evasive braking have the potential to reduce the number of intersection crashes. However, these systems will encounter many challenges caused by the complexity of real-world driving conditions. The purpose of this study is to use real-world naturalistic driving data to conduct an initial exploration of the potential challenges for future I-ADAS in straight crossing path (SCP), left turn across path/lateral direction (LTAP/LD), and left turn across path/opposite direction (LTAP/OD) crash configurations. Methods Intersection crashes were selected from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. The SHRP 2 dataset includes front-facing, driver-facing, rear-facing, and a hands/feet-facing video and vehicle speed, steering, accelerator, and brake time-series data. This data was reviewed to understand driver sightline obstructions, driver distractions, and initiation of driver responses. The estimated time to collision (TTC) from the precipitating event, defined as when either vehicle entered the intersection without the right-of-way, was computed based on the distance to the impact point divided by the current velocity of the subject vehicle. Results The median impact speed was 18.0 km/h for SCP and LTAP/LD crashes and 16.1 km/h for LTAP/OD crashes. The median TTC from the precipitating event was 1.35 s for SCP and LTAP/LD crashes and 1.44 s for LTAP/OD crashes. For SCP crashes, the three main sightline obstruction scenarios were slower vehicles traveling in the same direction waiting to turn right, vehicles in the closer crossing lane, and a parked truck. For LTAP/OD crashes, the sightline obstruction was often oncoming vehicles in a closer lane blocking the view of another vehicle. Conclusion Sightline obstructions could present a challenge for future I-ADAS to activate in SCP, LTAP/LD, and LTAP/OD crashes. This study utilized naturalistic driving data to complete a comprehensive analysis of intersection crashes, including driver distractions, evasive maneuvers, and sightline obstructions that can assist in the development of I-ADAS. This analysis is not possible with police-reported crash data only, which does not contain necessary details on the driver and surrounding environment.

Pedestrian- and bicyclist-involved crashes: Associations with spatial factors, pedestrian infrastructure, and equity impacts

Introduction: We analyze and compare the factors that influence the fatality of pedestrian and bicyclist involved crashes in New Jersey using available police-reported crash data between 2016 and 2020. Under three percent of crashes involve non-motorists statewide, but these account for about one third of all traffic fatalities in the state. Methods: Our analysis is broken down into five parts: we (1) analyze the relationship between minority and low-income communities and non-motorist involved crashes; (2) identify spatial differences between non-motorist involved crashes and non-motorist involved fatal crashes; (3) compare the factors affecting fatal pedestrian crashes in New Jersey and in four counties in southern New Jersey for which we have data on pedestrian infrastructure; (4) compare the factors affecting fatal pedestrian crashes and fatal cyclist crashes in New Jersey; and, (5) discuss priority areas for improving safety. Results: Crashes occur disproportionately more often in low-income communities. Moreover, we find that crashes are less likely to be geocoded if they take place in low-income and minority areas, a concerning finding considering that geocoded crashes are of paramount importance in identifying specific corridors for improvement. Light conditions, non-motorist age, posted speed, and vehicle type are significant factors influencing the fatality of non-motorist involved crashes. The proximity to a crosswalk or sidewalk is associated with decreased risk of a fatal crash for pedestrians. Cyclist crashes in low-income neighborhoods were more likely to be fatal – a finding that we attribute to lower access to bicycle facilities in low-income areas. Conclusions: We conclude with countermeasures, including a call for better data collection.

Understanding the new trends in pedestrian injury distribution and mechanism through data linkage and modeling

The objectives of this study were to 1) collect and analyze recent pedestrian crash cases for better understanding of the pedestrian injury distribution and mechanism, 2) use computational simulations to reconstruct pedestrian cases and estimate potential benefit of pedestrian automatic emergency braking (PedAEB) in reducing pedestrian injury risks, and 3) estimate how future pedestrian crash distribution might influence priorities for pedestrian protection.Analyses of national crash-injury dataset showed that the overall number of pedestrians in crashes as well as the serious and fatal pedestrian injuries in the U.S. have been increasing in recent years. Striking vehicle type has changed (i.e., decreased proportion of passenger cars and increase of SUVs and pickup trucks) from 20 years ago mirroring changes in the fleet distribution of vehicle sales. A total of 432 pedestrian injury cases were generated by linking the Michigan trauma data and police-reported crash data from 2013 to 2018. Among the linked cases, pickup trucks and SUVs were involved in crashes with more injuries across body regions. Notably, AIS 3+ chest injuries occur at almost the same rate as lower extremity injuries. A method, combining MADYMO simulations (n = 3,500), response surface model, and data mining, was developed to reconstruct 25 linked pedestrian crash cases to estimate the effectiveness of PedAEB. Based on national field data and MADYMO simulations, PedAEB was estimated to be effective in reducing the risk of head and lower extremity injuries but is relatively less effective in reducing the risk of chest injuries.The increased proportions of SUVs and pickup trucks in the vehicle fleet and the higher penetration of PedAEB may highlight the importance of future research into chest injury risk for pedestrian protection.

Post COVID-19 Transformation in the Frequency and Location of Traffic Crashes Involving Older Adults

Although numerous studies have been conducted to discover the spatial patterns of road crashes, relatively few have focused on the patterns of road crashes suffered by socially disadvantaged groups, while simultaneously accounting for urban environmental features. This study used advanced econometric (negative binomial regression) and spatial (geographically weighted Poisson regression) approaches to capture latent geographical diversity in crash patterns. The police-reported crash data for the over-65 population in metropolitan Adelaide, Australia, were investigated for two periods: before and after COVID-19. Using both spatial and nonspatial models, the effects of land use mix, population density, road network design, distance to the central business district, and accessibility of public transit on crash frequency, and location at the neighborhood level were investigated. The findings revealed that, in addition to sociodemographic factors, the aforementioned components had nonlinear effects in varied geographical contexts. Although the number of crashes fell by 20% during the periods studied, the fundamental reasons for such incidents did not change. The results of the study could assist academics and policy makers in Australia to better understand the multidimensional implications of the built environment on the road safety of the elderly—a vulnerable group in society who were disproportionately affected by the global pandemic. The hybrid technique presented in this research has the potential to be useful in other scenarios experiencing varying crash patterns.

Exploring injury severity of Non-Motor vehicle riders involving in traffic accidents using the generalized ordered logit model

This study aims to explore potential factors that affect non-motor vehicles (NMV) riders’ injury severity in accidents with motor vehicles (MV). Factors including human characteristics, vehicles, road features and environmental conditions were investigated. Police-reported crash data from the period of 2007 to 2017 in Xi’an, China were used to develop the generalized ordered logit models. Model results revealed that NMV riders’ injury severity was significantly influenced by rider’s Hukou, type of NMV, type of roadside protection, road structure, linearity, weather, accident time, light condition and visibility level. Besides, factors involving NMV rider’s age, compulsory third party insurance, type of MV, season, road type will increase their risk of suffering from serious/fatal injuries. Research findings are beneficial in helping policymakers to develop measures to improve NMV riders’ safety in China, such as isolating NMV and MV, educating drivers especially those from rural areas, and improving road and environment conditions.

Examination of Crash Rates and Injury Severity Before, During, and After Roadworks at High-Speed Regional Roads

Although work zones pose risks to roadworkers and motorists, undertaking roadworks is important to maintain and upgrade road networks. Many researchers have examined the associations between safety levels and roadwork activities and have produced inconsistent findings. Whereas some researchers have compared safety levels before and during roadwork activities, few compared the safety levels at three stages of roadworks: before, during, and after roadworks, to obtain a comprehensive understanding of the associations. In addition to the inconsistent findings, another significant gap exists in the literature: existing research on all three stages is dated and scarce. To address this important gap, this paper aims to examine the associations between safety levels and three stages of roadworks. Using police-reported crash data from twenty-four high-speed road segments in the regional areas of New South Wales, Australia, this paper presents findings from a statistical analysis of the data to understand how crash rates and injury severity levels changed across the three stages. Results showed that crash rates for all crashes, as well as for fatal and injury crashes, reduced significantly from the before period to the during period, and then further to the after period. Greater reductions in crash rates were also observed for crashes that did not involve a heavy vehicle than the heavy vehicle-involved crashes, crashes that were not related to speeding or fatigue issues than those with such issues, crashes that occurred during weekdays than weekends, and crashes that occurred at straight sections than at curved sections.

Linking Police and EMS Records: An Approach to Strengthen Bicyclist Injury Reporting

Bicyclist injuries are underreported in police crash databases. We explored the value of linking police-reported crash data with emergency medical services (EMS) data within the City of Milwaukee, WI. Using data from 2014 to 2016, we linked records by inspection (identical date, similar time of day, and similar roadway location) and found 154 matching records between the two databases (representing 41% of the 375 police crash records and 44% of the 348 EMS injury records). Matched records were more likely to involve fatal and severe injuries according to police-assessed injury ratings. The two datasets also provided different insights into bicyclist injury crashes. Injuries captured only by police reports were significantly more likely than injuries captured only by EMS to be along high-traffic streets and commercial districts and significantly less likely to be near parks. Nearly all police records described the driver and bicyclist movements (e.g., turning) and operating behaviors (e.g., failure to yield) that had contributed to the crash. In addition to capturing more bicyclist injury events, EMS records revealed additional information about their causes. Twenty-three percent of EMS narratives described falls. These falls involved intoxication, striking a curb, swerving to avoid automobiles or other bicyclists, doing tricks, and getting a tire caught in rail tracks. Another 11% described bicyclists striking objects, including a bus stop shelter, stop sign, fire hydrant, and fence. Although there are strengths and limitations to both datasets, linking police-reported crashes with EMS records produced a broader understanding of bicyclist injuries.

Syncope and subsequent traffic crash: A responsibility analysis.

Physicians are often asked to counsel patients about driving safety after syncope, yet little empirical data guides such advice. We identified a population-based retrospective cohort of 9,507 individuals with a driver license who were discharged from any of six urban emergency departments (EDs) with a diagnosis of 'syncope and collapse'. We examined all police-reported crashes that involved a cohort member as a driver and occurred between 1 January 2010 and 31 December 2016. We categorized crash-involved drivers as 'responsible' or 'non-responsible' for their crash using detailed police-reported crash data and a validated responsibility scoring tool. We then used logistic regression to test the hypothesis that recent syncope was associated with driver responsibility for crash. Over the 7-year study interval, cohort members were involved in 475 police-reported crashes: 210 drivers were deemed responsible and 133 drivers were deemed non-responsible for their crash; the 132 drivers deemed to have indeterminate responsibility were excluded from further analysis. An ED visit for syncope occurred in the three months leading up to crash in 11 crash-responsible drivers and in 5 crash-non-responsible drivers, suggesting that recent syncope was not associated with driver responsibility for crash (adjusted odds ratio, 1.31; 95%CI, 0.40-4.74; p = 0.67). However, all drivers with cardiac syncope were deemed responsible, precluding calculation of an odds ratio for this important subgroup. Recent syncope was not significantly associated with driver responsibility for traffic crash. Clinicians and policymakers should consider these results when making fitness-to-drive recommendations after syncope.