Increase in Motor Vehicle Crash Severity: An Unforeseen Consequence of COVID-19.

INTRODUCTION: The 2019 coronavirus (COVID-19) pandemic led to stay-at-home (SAH) orders in Pennsylvania targeted at reducing viral transmission. Limitations in population mobility under SAH have been associated with decreased motor vehicle collisions (MVC) and related injuries, but the impact of these measures on severity of injury remains unknown. The goal of this study is to measure the incidence, severity, and outcomes of MVC-related injuries associated with SAH in Pennsylvania. MATERIALS & METHODS: We conducted a retrospective geospatial analysis of MVCs during the early COVID-19 pandemic using a state-wide trauma registry. We compared characteristics of patients with MVC-related injuries admitted to Pennsylvania trauma centers during SAH measures (March 21-July 31, 2020) with those from the corresponding periods in 2018 and 2019. We also compared incidence of MVCs for each zip code tabulation area (ZCTA) in Pennsylvania for the same time periods using geospatial mapping. RESULTS: Of 15,550 trauma patients treated during the SAH measures, 3486 (22.4%) resulted from MVCs. Compared to preceding years, MVC incidence decreased 10% under SAH measures with no change in mortality rate. However, in ZCTA where MVC incidence decreased, there was a 16% increase in MVC injury severity. CONCLUSIONS: Stay-at-home orders issued in response to the COVID-19 pandemic in Pennsylvania were associated with significant changes in MVC incidence and severity. Identifying such changes may inform resource allocation decisions during future pandemics or SAH events.

Evidence of sample selectivity in highway injury-severity models: The case of risky driving during COVID-19

Research in highway safety continues to struggle to address two potentially important issues;the role that unobserved factors may play on resulting crash and injury-severity likelihoods, and the issue of identification in safety modeling caused by the self-selective sampling inherent in commonly used safety data (the fact that drivers in observed crashes are not a random sample of the driving population, with riskier drivers being over-represented in crash data bases). This paper addresses unobserved heterogeneity using mixing distributions and attempts to provide insight into the potential sample-selection problem by considering data before and during the COVID-19 pandemic. Based on a survey of vehicle usage (vehicle miles traveled) and subsequent statistical modeling, there is evi-dence that riskier drivers likely made up a larger proportion of vehicle miles traveled dur-ing the pandemic than before, suggesting that the increase in injury severities observed during COVID-19 could potentially be due to the over-representation of riskier drivers in observed crash data. However, by exploring Florida crash data before and during the pan-demic (and focusing on crashes where risky behaviors were observed), the empirical anal-ysis of observed crash data suggests (using random parameters multinomial logit models of driver-injury severities with heterogeneity in means and variances) that the observed increase in injury severity during the COVID-19 pandemic (calendar year 2020) was likely due largely to fundamental changes in driver behavior and less to changes in the sample selectivity of observed crash data. The findings of this paper provide some initial guidance to future work that can begin to more rigorously explore and assess the role of selectivity and resulting identification issues that may be present when using observed crash data.(c) 2022 Elsevier Ltd. All rights reserved.

Impact of easing COVID-19 lockdown restrictions on traumatic injuries in Riyadh, Saudi Arabia: one-year experience at a major trauma centre.

INTRODUCTION: Lockdown restrictions due to the COVID-19 pandemic have reduced the number of injuries recorded. However, little is known about the impact of easing COVID-19 lockdown restrictions on the nature and outcome of injuries. This study aims to compare injury patterns prior to and after the easing of COVID-19 lockdown restrictions in Saudi Arabia. METHOD: Data were collected retrospectively from the Saudi TraumA Registry for the period between March 25, 2019, and June 21, 2021. These data corresponded to three periods: March 2019-February 2020 (pre-restrictions, period 1), March 2020-June 2020 (lockdown, period 2), and July 2020-June 2021 (post easing of restrictions, period 3). Data related to patients' demographics, mechanism and severity of injury, and in-hospital mortality were collected and analysed. RESULTS: A total of 5,147 traumatic injury patients were included in the analysis (pre-restrictions n = 2593; lockdown n = 218; post easing of lockdown restrictions n = 2336). An increase in trauma cases (by 7.6%) was seen in the 30-44 age group after easing restrictions (n = 648 vs. 762, p < 0.01). Motor vehicle crashes (MVC) were the leading cause of injury, followed by falls in all the three periods. MVC-related injuries decreased by 3.1% (n = 1068 vs. 890, p = 0.03) and pedestrian-related injuries decreased by 2.7% (n = 227 vs. 143, p < 0.01); however, burn injuries increased by 2.2% (n = 134 vs. 174, p < 0.01) and violence-related injuries increased by 0.9% (n = 45 vs. 60, p = 0.05) post easing of lockdown restrictions. We observed an increase in in-hospital mortality during the period of 12 months after easing of lockdown restrictions-4.9% (114/2336) compared to 12 months of pre-lockdown period-4.3% (113/2593). CONCLUSION: This is one of the first studies to document trauma trends over a one-year period after easing lockdown restrictions. MVC continues to be the leading cause of injuries despite a slight decrease; overall injury cases rebounded towards pre-lockdown levels in Saudi Arabia. Injury prevention needs robust legislation with respect to road safety measures and law enforcement that can decrease the burden of traumatic injuries.

Amending the heston stochastic volatility model to forecast local motor vehicle crash rates: A case study of Washington, D.C

Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work and school commute patterns, crash rates can be determined with a reasonable degree of accuracy. However, this process becomes more complicated for an area that is frequently subject to peaks and troughs in traffic volume and crash events owing to exogenous events – for example, extreme weather – rather than typical commute patterns. One such area that is particularly exposed to exogenous events is Washington, D.C., which has seen a large rise in crash events between 2009 and 2020. In this study, we adopt a forecasting model that embeds heterogeneity and temporal instability in its estimates in order to improve upon forecasting models currently used in transportation and road safety research. Specifically, we introduce a stochastic volatility model that aims to capture the nuances associated with crash rates in Washington, D.C. We determine that this model can outperform conventional forecasting models, but it does not perform well in light of the unique travel patterns exhibited throughout the COVID-19 pandemic. Nevertheless, its adaptability to the idiosyncrasies of Washington, D.C. crash rates demonstrates its ability to accurately simulate localised crash rates processes, which can be further adapted in public policy contexts to form road safety targets. © 2022 The Author(s)

Evaluation of motor vehicle crash rates during and after the COVID-19-associated stay-at-home order in Connecticut.

INTRODUCTION: Recent research suggests that COVID-19 associated stay-at-home orders, or shelter-in-place orders, have impacted intra-and-interstate travel as well as motor vehicle crashes (crashes). We sought to further this research and to understand the impact of the stay-at-home order on crashes in the post order period in Connecticut. METHODS: We used a multiple-comparison group, interrupted time-series analysis design to compare crashes per 100 million vehicle miles traveled (VMT) per week in 2020 to the average of 2017-2019 from January 1-August 31. We stratified crash rate by severity and the number of vehicles involved. We modeled two interruption points reflecting the weeks Connecticut implemented (March 23rd, week 12) and rescinded (May 20th, week 20) its stay-at-home order. RESULTS: During the initial week of the stay-at-home order in Connecticut, there was an additional 28 single vehicle crashes compared to previous years (95% confidence interval (CI): [15.8, 36.8]). However, the increase at the order onset was not seen throughout the duration. Rescinding the stay-at-home order by and large did not result in an immediate increase in crash rates. Crash rates steadily returned to previous year averages during the post-stay-at-home period. Fatal crash rates were unaffected by the stay-at-home order and remained similar to previous year rates throughout the study duration. DISCUSSION: The initial onset of the stay-at-home order in Connecticut was associated with a sharp increase in the single vehicle crash rate but that increase was not sustained for the remainder of the stay-at-home order. Likely changes in driver characteristics during and after the order kept fatal crash rates similar to previous years.

So much nuance: A qualitative analysis of parental perspectives on child passenger safety.

OBJECTIVE: To explore barriers and facilitators to optimal child restraint system (CRS) use for diverse parents of newborn infants and to obtain input from parents on the use of technology-assisted remote car seat checks as tools for promoting optimal CRS use. METHODS: Parents were recruited using purposive sampling. Interviews were conducted with English- or Spanish-speaking parents with a full term newborn and regular access to a car. Interviews were conducted by phone, and recorded and transcribed verbatim. Interviews were conducted until thematic saturation was reached. Data were organized for analysis using Atlas.ti, and codes grouped by theme using constant comparison. RESULTS: 30 parents were enrolled. Barriers and facilitators to optimal CRS use were classified into three themes, as were thoughts on the pros and cons regarding remote car seat checks. Themes on barriers and facilitators included motor vehicle and CRS features (such as age and size of the motor vehicle and presence of the Lower Anchors and Tethers for Children LATCH system), resources (availability, accessibility, and accuracy of resources), and parental factors (parental emotions and characteristics). Themes related to pros and cons of remote car seat checks included the ability (and challenge) of remote car seat checks to identify and correct errors, the potential use of remote car seat checks in certain situations (such as CRS transitions and periods of growth), and convenience of remote car seat checks (including increased availability and ease of access). Subthemes with further detail were arranged within each theme identified. CONCLUSION: From a parent perspective, there are several identified barriers and facilitators of optimal CRS use. Although car seat checks were identified as a resource, in-person accessibility was an issue, and there were mixed opinions on technology-assisted remote car seat checks. These results provide a foundation for additional study on targeted interventions, including remote interventions for which there is an increased need due to the COVID-19 pandemic.