Non-benzodiazepine Hypnotics and Police-Reported Motor Vehicle Crash Risk among Older Adults: A Sequential Target Trial Emulation.

Non-benzodiazepine hypnotics ( "Z-drugs") are prescribed for insomnia, but might increase risk of motor vehicle crash (MVC) among older adults through prolonged drowsiness and delayed reaction times. We estimated the effect of initiating Z-drug treatment on the 12-week risk of MVC in a sequential target trial emulation. After linking New Jersey driver licensing and police-reported MVC data to Medicare claims, we emulated a new target trial each week (July 1, 2007 - October 7, 2017) in which Medicare fee-for-service beneficiaries were classified as Z-drug-treated or untreated at baseline and followed for an MVC. We used inverse probability of treatment and censoring weighted pooled logistic regression models to estimate risk ratios (RR) and risk differences with 95% bootstrap confidence limits (CLs). There were 257,554 person-trials, of which 103,371 were Z-drug-treated and 154,183 untreated, giving rise to 976 and 1,249 MVCs, respectively. The intention-to-treat RR was 1.06 (95%CLs 0.95, 1.16). For the per-protocol estimand, there were 800 MVCs and 1,241 MVCs among treated and untreated person-trials, respectively, suggesting a reduced MVC risk (RR 0.83 [95%CLs 0.74, 0.92]) with sustained Z-drug treatment. Z-drugs should be prescribed to older patients judiciously but not withheld entirely over concerns about MVC risk.

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.

Injury and fatality risks for child pedestrians and cyclists on public roads.

BACKGROUND: Pedestrians and cyclists are often referred to as "vulnerable road users," yet most research is focused on fatal crashes. We used fatal and nonfatal crash data to examine risk factors (i.e., relationship to an intersection, urbanicity, crash circumstances, and vehicle type) for police-reported pedestrian and cyclist injuries on public roads among children aged 0-9 and aged 10-19. We also compared risk factors among these two age groups with adults aged 20-29 and aged 30-39. METHODS: Crash data were obtained for 2016-2020 from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System for fatal crash injuries and Crash Report Sampling System for nonfatal crash injuries. We collected data on victim demographics, roadway, and vehicle- and driver-related factors. Descriptive analyses were conducted between and within pedestrian and cyclist victims. RESULTS: We analyzed 206,429 pedestrian injuries (36% in children aged 0-19) and 148,828 cyclist injuries (41% in children aged 0-19) from 2016 to 2020. Overall, child pedestrians had lower injury rates than adults, but children aged 10-19 had greater cycling crash rates than adults. Almost half of the pedestrian injuries in children aged 0-9 were "dart-out" injuries (43%). In the majority of the cyclist injuries, children in both age groups failed to yield to vehicles (aged 0-9 = 40% and aged 10-19 = 24%). For children and all ages included in the study, the fatality risk ratio was highest when pedestrians and cyclists were struck by larger vehicles, such as trucks and buses. Further exploration of roadway factors is presented across ages and transportation mode. CONCLUSION: Our findings on child, driver, vehicle, and roadway factors related to fatal and nonfatal pedestrian and cyclist injuries may help to tailor prevention efforts for younger and older children.

State cannabis laws and cannabis positivity among fatally injured drivers.

BACKGROUND: As of November 8, 2023, 24 states and the District of Columbia have legalized cannabis for both recreational and medical use (RMCL-states), 14 states have legalized cannabis for medical use only (MCL-states) and 12 states have no comprehensive cannabis legislation (NoCL-states). As more states legalize cannabis for recreational use, it is critical to understand the impact of such policies on driving safety. METHODS: Using the 2019 and 2020 Fatality Analysis Reporting System data, we performed multivariable logistic regression modeling to explore the association between state level legalization status and cannabis positivity using toxicological testing data for 14,079 fatally injured drivers. We performed a sensitivity analysis by including multiply imputed toxicological testing data for the 14,876 eligible drivers with missing toxicological testing data. RESULTS: Overall, 4702 (33.4%) of the 14,079 fatally injured drivers tested positive for cannabis use. The prevalence of cannabis positivity was 30.7% in NoCL-states, 32.8% in MCL-states, and 38.2% in RMCL-states (p < 0.001). Compared to drivers fatally injured in NoCL-states, the adjusted odds ratios of testing positive for cannabis were 1.09 (95% confidence interval: 0.99, 1.19) for those fatally injured in MCL-states and 1.54 (95% confidence interval: 1.34, 1.77) for those fatally injured in RMCL-states. Sensitivity analysis yielded similar results. CONCLUSIONS: Over one-third of fatally injured drivers tested positive for cannabis use. Drivers fatally injured in states with laws permitting recreational use of cannabis were significantly more likely to test positive for cannabis use than those in states without such laws. State medical cannabis laws had little impact on the odds of cannabis positivity among fatally injured drivers.

Alcohol-related non-fatal motor vehicle crash injury in the US from 2019 to 2022.

Background: Information on recent alcohol-related non-fatal motor vehicle crash (MVC) injuries is limited.Objectives: To analyze alcohol-related non-fatal MVC injuries, 2019-2022, considering COVID-19 and Stay-at-Home policies.Methods: State-level counts of alcohol-related non-fatal MVC injuries (involving individuals age 15+) from Emergency Medical Services data in 18 US states, chosen for comprehensive coverage, were analyzed for the annual rate. The total non-fatal MVC injury count in each state served as the denominator. We used analysis of variance to evaluate annual rate changes from 2019 to 2022 and used robust Poisson regression to compare annual mean rates to the 2019 baseline, pre-pandemic, excluding Quarter 1 due to COVID-19's onset in Quarter 2. Additional Poisson models compared rate changes by 2020 Stay-at-Home policies.Results: Data from 18 states were utilized (N = 1,487,626, 49.5% male). When evaluating rate changes of alcohol-related non-fatal MVC injuries from period 1 (Q2-4 2019) through period 4 (Q2-4 2022), the rate significantly increased from period 1 (2019) to period 2 (2020) by 0.024 (p = .003), then decreased from period 2 to period 4 (2022) by 0.016 (p = .04). Compared to the baseline (period 1), the rate in period 2 was 1.27 times higher. States with a 2020 Stay-at-Home policy, compared to those without, had a 30% lower rate (p = .05) of alcohol-related non-fatal MVC injuries. States with partial and mandatory Stay-at-Home policies had a 5.2% (p = .01) and 10.5% (p < .001) annual rate decrease, respectively.Conclusion: Alcohol-related non-fatal MVC injury rates increased initially (2019-2020) but decreased thereafter (2020-2022). Stay-at-home policies effectively reduced these rates.

A systematic review of the impact of brain tumours on risk of motor vehicle crashes.

PURPOSE: Brain tumours are associated with neurocognitive impairments that are important for safe driving. Driving is vital to maintaining patient autonomy, despite this there is limited research on driving capacity amongst patients with brain tumours. The purpose of this review is to examine MVC risk in patients with brain tumours to inform development of clearer driving guidelines. METHODS: A systematic review was performed using Medline and EMBASE. Observational studies were included. The outcome of interest was MVC or measured risk of MVC in patients with benign or malignant brain tumours. Descriptive analysis and synthesis without meta-analysis were used to summarise findings. A narrative review of driving guidelines from Australia, United Kingdom and Canada was completed. RESULTS: Three studies were included in this review. One cohort study, one cross-sectional study and one case-control study were included (19,135 participants) across United States and Finland. One study evaluated the incidence of MVC in brain tumour patients, revealing no difference in MVC rates. Two studies measured MVC risk using driving simulation and cognitive testing. Patients found at higher risk of MVC had greater degrees of memory and visual attention impairments. However, predictive patient and tumour characteristics of MVC risk were heterogeneous across studies. Overall, driving guidelines had clear recommendations on selected conditions like seizures but were vague surrounding neurocognitive deficits. CONCLUSION: Limited data exists regarding driving behaviour and MVC incidence in brain tumour patients. Existing guidelines inadequately address neurocognitive complexities in this group. Future studies evaluating real-world data is required to inform development of more applicable driving guidelines. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO 2023 CRD42023434608.

Impact of alcohol driving-while-impaired license suspension duration on future alcohol-related license events and motor vehicle crash involvement in North Carolina, 2007 to 2016.

BACKGROUND/PURPOSE: License suspensions are a strategy to address alcohol-impaired driving behavior and recidivism following an alcohol driving while impaired (alcohol-DWI) conviction. Little is known about the specific impacts of conviction-related suspensions on safety outcomes and given recent fluctuations in alcohol-impaired driving behavior, crashes, and suspension trends, updated and focused assessments of this intervention are necessary. This study aimed to 1) examine the association between type of recent alcohol-DWI suspension and having a secondary alcohol-related license outcome and/or future crash event in North Carolina (NC) between 2007 and 2016; and 2) assess potential modification of these associations by race/ethnicity. METHODS: We used linked NC licensing data, NC crash data, and county-level contextual data from a variety of data sources. We compared individuals ages 21 to 64 who sustained initial (1-year) versus repeat (4-year) suspensions for alcohol-related license and crash involvement outcomes. We estimated unadjusted and adjusted hazard ratios (aHRs) using Cox proportional hazards models and produced Kaplan-Meier (KM) survival curves using a three-year follow-up period. After observing statistically significant modification by race/ethnicity, we calculated stratified aHRs for each outcome (Black and White subgroups only, as other subgroups had low numbers of outcomes). RESULTS: 122,002 individuals sustained at least one alcohol-DWI conviction suspension (117,244 initial, 4,758 repeat). Adjusted KM survival curves indicated that within three years of the index suspension, the predicted risks of having a license outcome and crash outcome were about 8 % and 15 %, respectively, among individuals with an initial suspension and 5 % and 10 %, respectively, among individuals with a repeat suspension. After adjusting for potential confounding, we found that compared to those with an initial suspension, those with repeat suspensions had a lower incidence of future license (aHR: 0.49; 95 % CI: 0.42, 0.57) and crash outcomes (aHR: 0.67; 95 % CI: 0.60, 0.75). Among Black individuals, license outcome incidence was 162 % lower among repeat versus initial index suspension groups (aHR: 0.38; 95 % CI: 0.26, 0.55), while for White individuals, the incidence was 87 % lower (aHR: 0.54; 95 % CI: 0.45, 0.64). Similarly, crash incidence for repeat versus initial suspensions among Black individuals was 56 % lower (aHR: 0.64; 95 % CI: 0.50, 0.83), while only 39 % lower among White individuals (aHR: 0.72; 95 % CI: 0.63, 0.81). CONCLUSIONS: Decreased incidence of both license and crash outcomes were observed among repeat versus initial index suspensions. The magnitude of these differences varied by race/ethnicity, with larger decreases in incidence among Black compared to White individuals. Future research should examine the underlying mechanisms leading to alcohol-impaired driving behavior, convictions, recidivism, and crashes from a holistic social-ecological perspective so that interventions are designed to both improve road safety and maximize other critical public health outcomes, such as access to essential needs and services (e.g., healthcare and employment).

A systematic review and meta-analysis of data linkage between motor vehicle crash and hospital-based datasets.

Motor vehicle crash data linkage has emerged as a vital tool to better understand the injury outcomes and the factors contributing to crashes. This systematic review and meta-analysis aims to explore the existing knowledge on data linkage between motor vehicle crashes and hospital-based datasets, summarize and highlight the findings of previous studies, and identify gaps in research. A comprehensive and systematic search of the literature yielded 54 studies for a qualitative analysis, and 35 of which were also considered for a quantitative meta-analysis. Findings highlight a range of viable methodologies for linking datasets, including manual, deterministic, probabilistic, and integrative methods. Designing a linkage method that integrates different algorithms and techniques is more likely to result in higher match rate and fewer errors. Examining the results of the meta-analysis reveals that a wide range of linkage rates were reported. There are several factors beyond the approach that affect the linkage rate including the size and coverage of both datasets and the linkage variables. Gender, age, crash type, and roadway geometry at the crash site were likely to be associated with a record's presence in a linked dataset. Linkage rate alone is not the only important metric and when linkage rate is used as a metric in research, both police and hospital rates should be reported. This study also highlights the importance of examining and accounting for population and bias introduced by linking two datasets.

A Lightweight Pre-Crash Occupant Injury Prediction Model Distills Knowledge From Its Post-Crash Counterpart.

Accurate occupant injury prediction in near-collision scenarios is vital in guiding intelligent vehicles to find the optimal collision condition with minimal injury risks. Existing studies focused on boosting prediction performance by introducing deep-learning models but encountered computational burdens due to the inherent high model complexity. To better balance these two traditionally contradictory factors, this study proposed a training method for pre-crash injury prediction models, namely, knowledge distillation (KD)-based training. This method was inspired by the idea of knowledge distillation, an emerging model compression method. Technically, we first trained a high-accuracy injury prediction model using informative post-crash sequence inputs (i.e., vehicle crash pulses) and a relatively complex network architecture as an experienced "teacher". Following this, a lightweight pre-crash injury prediction model ("student") learned both from the ground truth in output layers (i.e., conventional prediction loss) and its teacher in intermediate layers (i.e., distillation loss). In such a step-by-step teaching framework, the pre-crash model significantly improved the prediction accuracy of occupant's head abbreviated injury scale (AIS) (i.e., from 77.2% to 83.2%) without sacrificing computational efficiency. Multiple validation experiments proved the effectiveness of the proposed KD-based training framework. This study is expected to provide reference to balancing prediction accuracy and computational efficiency of pre-crash injury prediction models, promoting the further safety improvement of next-generation intelligent vehicles.

Migraine headaches are associated with motor vehicle crashes and driving habits among older drivers: Prospective cohort study.

BACKGROUND: Migraine headache is common in older adults, often causing symptoms that may affect driving safety. This study examined associations of migraine with motor vehicle crashes (MVCs) and driving habits in older drivers and assessed modification of associations by medication use. METHODS: In a multi-site, prospective cohort study of active drivers aged 65-79 (53% female), we assessed prevalent migraine (i.e., ever had migraine, reported at enrollment), incident migraine (diagnosis first reported at a follow-up visit), and medications typically used for migraine prophylaxis and treatment. During 2-year follow-up, we recorded self-reported MVCs and measured driving habits using in-vehicle GPS devices. Associations of prevalent migraine with driving outcomes were estimated in multivariable mixed models. Using a matched design, associations of incident migraine with MVCs in the subsequent year were estimated with conditional logistic regression. Interactions between migraine and medications were tested in all models. RESULTS: Of 2589 drivers, 324 (12.5%) reported prevalent migraine and 34 (1.3%) incident migraine. Interactions between migraine and medications were not statistically significant in any models. Prevalent migraine was not associated with MVCs in the subsequent 2 years (adjusted OR [aOR] = 0.98; 95% CI: 0.72, 1.35), whereas incident migraine significantly increased the odds of having an MVC within 1 year (aOR = 3.27; 1.21, 8.82). Prevalent migraine was associated with small reductions in driving days and trips per month and increases in hard braking events in adjusted models. CONCLUSION: Our results suggest substantially increased likelihood of MVCs in the year after newly diagnosed migraine, indicating a potential need for driving safety interventions in these patients. We found little evidence for MVC risk or substantial changes in driving habits associated with prevalent migraine. Future research should examine timing, frequency, and severity of migraine diagnosis and symptoms, and use of medications specifically prescribed for migraine, in relation to driving outcomes.

Risks of motor vehicle crash before and during pregnancy: A population-based controlled study in Taiwan.

BACKGROUND: Changes in risk of motor vehicle crashes (MVCs) during pregnancy are less known, and very few studies have assessed this issue by using unselected population-based datasets and adopting a before-and-during design. The study aimed to address the risk of MVC events in association with pregnancy using a national pregnant women cohort in Taiwan. METHODS: We conducted a self-matched design in which each woman served as a driver before and during pregnancy. A total of 1,372,664 pregnant women with live birth(s) at 18-50 years of age between 2008 and 2017 were identified from the Birth Notification dataset. MVC events as a driver were ascertained from the Police-reported Traffic Accident Registry dataset. We calculated the rate ratio (RR) with a 95 % confidence interval (CI) using the conditional Poisson regression model to compare the MVC event rates between pre-pregnancy and pregnancy periods. RESULTS: The overall rate of MVC events was significantly reduced during pregnancy (RR = 0.69, 95 % confidence interval (CI) = 0.68-0.71). Mothers with alcoholism during pregnancy were associated with an increased RR at 2.00 but with a very wide CI. Reduction in RR was primarily attributed to the reduced MVC event rate involving scooter crashes (0.60, 95 % CI = 0.58-0.62). CONCLUSION: Although MVC event rates decreased during women became pregnant, many women drivers were still involved in MVCs during pregnancy. Their potential maternal and perinatal conditions along with their offspring's health outcomes need further investigations.

Promoting transportation safety in adolescence: the drivingly randomized controlled trial.

BACKGROUND: The impact of young drivers' motor vehicle crashes (MVC) is substantial, with young drivers constituting only 14% of the US population, but contributing to 30% of all fatal and nonfatal injuries due to MVCs and 35% ($25 billion) of the all medical and lost productivity costs. The current best-practice policy approach, Graduated Driver Licensing (GDL) programs, are effective primarily by delaying licensure and restricting crash opportunity. There is a critical need for interventions that target families to complement GDL. Consequently, we will determine if a comprehensive parent-teen intervention, the Drivingly Program, reduces teens' risk for a police-reported MVC in the first 12 months of licensure. Drivingly is based on strong preliminary data and targets multiple risk and protective factors by delivering intervention content to teens, and their parents, at the learner and early independent licensing phases. METHODS: Eligible participants are aged 16-17.33 years of age, have a learner's permit in Pennsylvania, have practiced no more than 10 h, and have at least one parent/caregiver supervising. Participants are recruited from the general community and through the Children's Hospital of Philadelphia's Recruitment Enhancement Core. Teen-parent dyads are randomized 1:1 to Drivingly or usual practice control group. Drivingly participants receive access to an online curriculum which has 16 lessons for parents and 13 for teens and an online logbook; website usage is tracked. Parents receive two, brief, psychoeducational sessions with a trained health coach and teens receive an on-road driving intervention and feedback session after 4.5 months in the study and access to DriverZed, the AAA Foundation's online hazard training program. Teens complete surveys at baseline, 3 months post-baseline, at licensure, 3months post-licensure, 6 months post-licensure, and 12 months post-licensure. Parents complete surveys at baseline, 3 months post-baseline, and at teen licensure. The primary end-point is police-reported MVCs within the first 12 months of licensure; crash data are provided by the Pennsylvania Department of Transportation. DISCUSSION: Most evaluations of teen driver safety programs have significant methodological limitations including lack of random assignment, insufficient statistical power, and reliance on self-reported MVCs instead of police reports. Results will identify pragmatic and sustainable solutions for MVC prevention in adolescence. TRIAL REGISTRATION: ClinicalTrials.gov # NCT03639753.

Using motor vehicle crash records for injury surveillance and research in agriculture and forestry.

PROBLEM: Fatal injuries in the agriculture, forestry, and fishing sector (AgFF) outweigh those across all sectors in the United States. Transportation-related injuries are among the top contributors to these fatal events. However, traditional occupational injury surveillance systems may not completely capture crashes involving farm vehicles and logging trucks, specifically nonfatal events. METHODS: The study aimed to develop an integrated database of AgFF-related motor-vehicle crashes for the southwest (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) and to use these data to conduct surveillance and research. Lessons learned during the pursuit of these aims were cataloged. Activities centered around the conduct of traditional statistical and geospatial analyses of structured data fields and natural language processing of free-text crash narratives. RESULTS: The structured crash data in each state include fields that allowed farm vehicles or equipment and logging trucks to be identified. The variable definitions and coding were not consistent across states but could be harmonized. All states recorded data fields pertaining to person, vehicle, and crash/environmental factors. Structured data supported the construction of crash severity models and geospatial analyses. Law enforcement provided additional details on crash causation in free-text narratives. Crash narratives contained sufficient text to support viable machine learning models for farm vehicle or equipment crashes, but not for logging truck narratives. DISCUSSION: Crash records can help to fill research and surveillance gaps in AgFF in the southwest region. This supports traffic safety's evolution to the current Safe System paradigm. There is a conceptual linkage between the Safe System and Total Worker Health approaches, providing a bridge between traffic safety and occupational health. PRACTICAL APPLICATIONS: Despite limitations, crash records can be an important component of injury surveillance for events involving AgFF vehicles. They also can be used to inform the selection and evaluation of traffic countermeasures and behavioral interventions.

Major trauma in pregnancy: prediction of maternal and perinatal adverse outcomes.

BACKGROUND: Trauma, largely the consequence of motor vehicle crashes, is the leading cause of pregnancy-associated maternal mortality. Prediction of adverse outcomes has been difficult in pregnancy given the infrequent occurrence of traumatic events and anatomic considerations unique to pregnancy. The injury severity score, an anatomic scoring system with weighting dependent on severity and anatomic region of injury, is used in the prediction of adverse outcomes in the nonpregnant population but has yet to be validated in pregnancy. OBJECTIVE: This study aimed to estimate the associations between risk factors and adverse pregnancy outcomes after major trauma in pregnancy and to develop a clinical prediction model for adverse maternal and perinatal outcomes. STUDY DESIGN: This was a retrospective analysis of a cohort of pregnant patients who sustained major trauma and who were admitted to 1 of 2 level 1 trauma centers. Three composite adverse pregnancy outcomes were evaluated, namely adverse maternal outcomes and short- and long-term adverse perinatal outcomes, defined as outcomes occurring within the first 72 hours of the traumatic event or encompassing the entire pregnancy. Bivariate analyses were performed to estimate the associations between clinical or trauma-related variables and adverse pregnancy outcomes. Multivariable logistic regression analyses were performed to predict each adverse pregnancy outcome. The predictive performance of each model was estimated using receiver operating characteristic curve analyses. RESULTS: A total of 119 pregnant trauma patients were included, 26.1% of whom met the severe adverse maternal pregnancy outcome criteria, 29.4% of whom met the severe short-term adverse perinatal pregnancy outcome definition, and 51.3% of whom met the severe long-term adverse perinatal pregnancy outcome definition. Injury severity score and gestational age were associated with the composite short-term adverse perinatal pregnancy outcome with an adjusted odds ratio of 1.20 (95% confidence interval, 1.11-1.30). The injury severity score was solely predictive of the adverse maternal and long-term adverse perinatal pregnancy outcomes with odds ratios of 1.65 (95% confidence interval, 1.31-2.09) and 1.14 (95% confidence interval, 1.07-1.23), respectively. An injury severity score ≥8 was the best cutoff for predicting adverse maternal outcomes with 96.8% sensitivity and 92.0% specificity (area under the receiver operating characteristic curve, 0.990±0.006). An injury severity score ≥3 was the best cutoff for the short-term adverse perinatal outcomes, which correlates with a 68.6% sensitivity and 65.1% specificity (area under the receiver operating characteristic curve, 0.755±0.055). An injury severity score ≥2 was the best cutoff for the long-term adverse perinatal outcomes, yielding a 68.3% sensitivity and 72.4% specificity (area under the receiver operating characteristic curve, 0.763±0.042). CONCLUSION: For pregnant trauma patients, an injury severity score of ≥8 was predictive of severe adverse maternal outcomes. Minor trauma in pregnancy, defined in this study as an injury severity score <2, was not associated with maternal or perinatal morbidity or mortality. These data can guide management decisions for pregnant patients who present after trauma.

A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on motor vehicle crashes: Logistic regression approach.

OBJECTIVE: Chest injuries that occur in motor vehicle crashes (MVCs) include rib fractures, pneumothorax, hemothorax, and hemothorax depending on the injury mechanism. Many risk factors are associated with serious chest injuries from MVCs. The Korean In-Depth Accident Study database was analyzed to identify risk factors associated with motor vehicle occupants' serious chest injury. METHODS: Among 3,697 patients who visited the emergency room in regional emergency medical centers after MVCs between 2011 and 2018, we analyzed data from 1,226 patients with chest injuries. Vehicle damage was assessed using the Collision Deformation Classification (CDC) code and images of the damaged vehicle, and trauma scores were used to determine injury severity. Serious chest injury was defined as an Abbreviated Injury Scale (AIS) score for the chest code was more than 3. The patients were divided into two groups: serious chest injury patients with MAIS ≥ 3 and those with non-serious chest injury with MAIS < 3. A predictive model to analyze the factors affecting the presence of serious chest injury in the occupants on MVCs was constructed by a logistic regression analysis. RESULTS: Among the 1,226 patients with chest injuries, 484 (39.5%) had serious chest injuries. Patients in the serious group were older than those in the non-serious group (p=.001). In analyses based on vehicle type, the proportion of light truck occupants was higher in the serious group than in the non-serious group (p=.026). The rate of seatbelt use was lower in the serious group than in the non-serious group (p=.008). The median crush extent (seventh column of the CDC code) was higher in the serious group than in the non-serious group (p<.001). Emergency room data showed that the rates of intensive care unit (ICU) admission and death were higher among patients with serious injuries (p<.001). Similarly, the general ward/ICU admission data showed that the transfer and death rates were higher in patients with serious injuries (p<.001). The median ISS was higher in the serious group than in the non-serious group (p<.001). A predictive model was derived based on sex, age, vehicle type, seating row, belt status, collision type, and crush extent. This predictive model had an explanatory power of 67.2% for serious chest injuries. The model was estimated for external validation using the confusion matrix by applying the predictive model to the 2019 and 2020 data of the same structure as the data at the time of model development in the KIDAS database. CONCLUSIONS: Although this study had a major limitation in that the explanatory power of the predictive model was weak due to the small number of samples and many exclusion conditions, it was meaningful in that it suggested a model that could predict serious chest injuries in motor vehicle occupants (MVOs) based on actual accident investigation data in Korea. Future studies should yield more meaningful results, for example, if the chest compression depth value is derived through the reconstruction of MVCs using accurate collision speed values, and better models can be developed to predict the relationship between these values and the occurrence of serious chest injury.

Traffic violator school masked convictions: California finally got it right.

INTRODUCTION: The negative traffic safety impact of California's prior traffic violator school (TVS) citation dismissal policy is well documented in past California TVS evaluations. METHOD: Using advanced inferential statistical techniques, the current study evaluated the substantive changes to California's traffic violator school program as required by California Assembly Bill (AB) 2499. The program changes implemented by AB 2499 appear to be associated with a specific deterrent effect as evidenced by a reliable and statistically significant reduction in subsequent traffic crashes of those receiving a masked TVS conviction as opposed to a countable conviction. RESULTS: The results suggest that this relationship exists primarily among TVS drivers with less elevated prior records. The change in status from a TVS citation dismissal to a TVS masked conviction has reduced the negative traffic safety impact of the TVS citation dismissal policy in effect prior to the implementation of AB 2499. Several recommendations are offered to enhance the positive traffic safety impact of the TVS program by further combining its educational elements with the state's post license control program by way of the Negligent Operator Treatment System. PRACTICAL APPLICATIONS: The findings and recommendations have implications to all states and jurisdictions utilizing pre-conviction diversion programs and/or demerit point systems associated with traffic violations.

Increase in trauma volume as compared to emergency department volume during the COVID-19 pandemic.

BACKGROUND: During the months between April through June 2020 when we experienced the largest number of COVID-19 patients in our hospital, the volume of patients in the Emergency Department (ED) was decreased by more than 30%. In contrast to most early reports we did not notice a decrease in trauma volume during this time period. MATERIALS AND METHODS: We compared trauma patients presenting to our Level III Trauma Center, between April 2019 through June 2019 to those presenting from April 2020 through June 2020, the initial surge in COVID-19 patients. We compared ground level falls (GLF), motor vehicle crashes (MVC), bicycle and skateboard crashes, assault, and other. RESULTS: There was a 13% increase in trauma patients presenting during the study period in 2020 as compared to 2019, and the total number of trauma patients as a percentage of total ED patients also increased 269/9235 (2.9%) to 308/6216 (5.0%), P < 0.0001. There was no significant difference in demographics or outcome data between the trauma patients presenting during the two time periods. Although traffic decreased by more than 40%, the number of MVC's was similar. CONCLUSION: The volume of patients presenting to our Trauma Center as compared to the total ED volume increased during the time period from April through June 2020 versus the year just prior to the COVID-19 pandemic. Despite the fact that the total traffic volume decreased more than 40 percent between these two time periods, the actual number of motor vehicle crashes remained similar.

Spinal injury rates and specific causation in motor vehicle collisions.

Motor vehicle collisions (MVCs) are a leading cause of acute spinal injuries. Chronic spinal pathologies are common in the population. Thus, determining the incidence of different types of spinal injuries due to MVCs and understanding biomechanical mechanism of these injuries is important for distinguishing acute injuries from chronic degenerative disease. This paper describes methods for determining causation of spinal pathologies from MVCs based on rates of injury and analysis of the biomechanics require to produce these injuries. Rates of spinal injuries in MVCs were determined using two distinct methodologies and interpreted using a focused review of salient biomechanical literature. One methodology used incidence data from the Nationwide Emergency Department Sample and exposure data from the Crash Report Sample System supplemented with a telephone survey to estimate total national exposure to MVC. The other used incidence and exposure data from the Crash Investigation Sampling System. Linking the clinical and biomechanical findings yielded several conclusions. First, spinal injuries caused by an MVC are relatively rare (511 injured occupants per 10,000 exposed to an MVC), which is consistent with the biomechanical forces required to generate injury. Second, spinal injury rates increase as impact severity increases, and fractures are more common in higher-severity exposures. Third, the rate of sprain/strain in the cervical spine is greater than in the lumbar spine. Fourth, spinal disc injuries are extremely rare in MVCs (0.01 occupants per 10,000 exposed) and typically occur with concomitant trauma, which is consistent with the biomechanical findings 1) that disc herniations are fatigue injuries caused by cyclic loading, 2) the disc is almost never the first structure to be injured in impact loading unless it is highly flexed and compressed, and 3) that most crashes involve predominantly tensile loading in the spine, which does not cause isolated disc herniations. These biomechanical findings illustrate that determining causation when an MVC occupant presents with disc pathology must be based on the specifics of that presentation and the crash circumstances and, more broadly, that any causation determination must be informed by competent biomechanical analysis.

Associations of cannabis use with motor vehicle crashes and traffic stops among older drivers: AAA LongROAD study.

BACKGROUND: Acute cannabis use is associated with a higher risk of motor vehicle crashes (MVC). This study aimed to determine if self-reported past-year cannabis use is associated with MVC or traffic stops among older drivers. METHODS: This cross-sectional analysis used data from a multi-center study enrolling active drivers aged 65-79 years. Data regarding cannabis use, MVC, and traffic stops (i.e., being pulled over by police, whether ticketed or not) within the previous 12 months were collected through participant interviews. Log-binomial regression models examined associations of past-year cannabis use with MVC and traffic stops, adjusting for site and sociodemographic and mental health characteristics. RESULTS: Of 2,095 participating older drivers, 186 (8.88%) used cannabis in the past year but only 10 (<0.5%) within an hour before driving in the last 30 days; 11.41% reported an MVC and 9.45% reported a traffic stop. Past-year cannabis users had a higher prevalence of MVC (adjusted prevalence ratio [aPR] = 1.38; 95%CI: 0.96, 2.00; p = 0.086) and traffic stops (aPR = 1.58; 1.06, 2.35; p = 0.024). CONCLUSIONS: Past-year cannabis use was associated with increased traffic stops, which are correlated modestly with increased MVC in past studies and may indicate impaired driving performance. We did not find a statistically significant association of past-year cannabis use with MVC, which may indicate limited sustained effects on driving performance from periodic use among older adults, who report rarely driving immediately after use.

Piloting a hospital-based road traffic injury surveillance system in Nairobi County, Kenya, 2018-2019.

BACKGROUND: Kenya's estimated road traffic injury (RTI) death rate is 27.8/100,000 population, which is 1.5 times the global rate. Some RTI data are collected in Kenya; however, a systematic and integrated surveillance system does not exist. Therefore, we adopted and modified the World Health Organization's injury surveillance guidelines to pilot a hospital-based RTI surveillance system in Nairobi County, Kenya. METHODS: We prospectively documented all RTI cases presenting at two public trauma hospitals in Nairobi County from October 2018-April 2019. RTI cases were defined as injuries involving ≥1 moving vehicles on public roads. Demographics, injury circumstances, and outcome information were collected using standardized case report forms. The Kampala Trauma Score (KTS) was used to assess injury severity. RTI cases were characterized with descriptive statistics. RESULTS: Of the 1,840 RTI cases reported during the seven-month period, 73.2% were male. The median age was 29.8 years (range 1-89 years). Forty percent (n = 740) were taken to the hospital by bystanders. Median time for hospital arrival was 77 min. Pedestrians constituted 54.1% (n = 995) of cases. Of 400 motorcyclists, 48.0% lacked helmets. Similarly, 65.7% of bicyclists (23/35) lacked helmets. Among 386 motor vehicle occupants, 59.6% were not using seat belts (19.9% unknown). Seven percent of cases (n = 129) reported alcohol use (49.0% unknown), and 8.8% (n = 161) reported mobile phone use (59.7% unknown). Eleven percent of cases (n = 199) were severely injured (KTS <11), and 220 died. CONCLUSION: We demonstrated feasibility of a hospital-based RTI surveillance system in Nairobi County. Integrating information from crash scenes and hospitals can guide prevention.