Bicycle Injuries Research Articles

Exploring the heterogeneous effects of zonal factors on bicycle injury severity: latent class clustering analysis and partial proportional odds models

Despite the benefits of cycling being widely accepted, bicycle safety—especially severe injury—has received increasing attention due to the vulnerability of bicyclists on the road. Factors contributing to varying bicycle injury severity have been identified in the literature. For the zonal factors, variables related to sociodemographic and household characteristics, built environments, land use, and traffic conditions are considered. However, it is rare that the heterogeneity and hierarchal features of bicycle injury severity are simultaneously considered. This study contributes to the literature by investigating the heterogeneous effects of zonal factors on varying bicycle injury severity, using a 3-year crash data set from the Lower Layer Super Output Areas of London. A combination of latent class clustering and partial proportional odds methods was developed. First, five subgroups of bicycle crashes were identified based on the latent class clustering method. Afterward, partial proportional models were developed separately for different clusters. Results indicate that a series of factors is found to be associated with the occurrence of severe bicycle injuries. However, effects of these factors could be distinctive among different clusters. For example, some factors only have significant impacts in the specific crash clusters. Furthermore, heterogeneous effects of the same factors in one or different clusters are discovered. The findings of this study can be helpful for the development of cycle infrastructures, traffic management, and safety education that can enhance the risk perception of bicyclists and reduce the occurrence of severe bicycle injuries.

Bicycle Injury Prevention Education Using 360° Virtual Reality Experiences of Accidents and Computer-Based Activity.

Injury prevention programs for children and adolescents need to be immersive and interactive. This study assessed a bicycle injury prevention program using technology-based education based on the Activated Health Education model and evaluated its effect on environmental factor awareness and attitude toward helmet-wearing. Using virtual reality technology, elementary and middle school students could experience simulated bicycle accidents. It was followed by an awareness phase that included a 30-min lecture where students self-learned and discussed risk-preventive factors. Students then developed user-created content and customized helmets they were given. We assessed students before the program, immediately afterward, and one month after the program. The number of respondents who said they were aware of surrounding bicycle lanes increased from 75.3% (pre-program) to 92.5% (one month after). Those who said they wore helmets often or always rose from 14.3% (pre-program) to 32.5% (one month later). The number needed to treat helmet-wearing behavior was approximately four, meaning that four people were required to participate in the program to have an impact on one person's helmet-wearing. We found that virtual reality and computer-based activities can help children and adolescents experience bicycle accidents, be aware of risk factors, and change their behaviors responsibly.

Risk prediction and factor analysis of rider’s injury severity in passenger car and E-bike accidents based on interpretable machine learning

Casualties and property losses caused by the passenger car and electric bicycle crash accidents increased year by year. Assessment of the relevant risk factors of injury severity in passenger car and electric bicycle crashes could help to mitigate crash severity. This study uses an emerging machine learning method to predict the relationship between the risk factors and bicyclist accident injury severity in passenger car-electric bicycle collision accidents. The model’s performance is compared and evaluated based on accuracy, precision, recall, F1-Score, area under curve (AUC), and receiver operating characteristic curve (ROC). An interpretable machine learning framework Shapley additive explanations (SHAP) is used to further analyze the relationship between risk factors and bicyclist injury severity. It is found that we can adopt the light gradient boosting machine (LightGBM) algorithm after hyper-parameter optimization to get the highest accuracy (94.85%), precision (95.2%), recall (94.9%), F1-Score (95%), and AUC (0.993) based on the accident data of electric bicycles and passenger cars in the China in-depth accident study dataset from 2014 to 2018. The model can be used to assess new accident cases based on the model learning rate. There are some new findings in the aspects of bicyclists’ physical factors and electric vehicle characteristics. The throwing distance of the bicyclist has a positive impact on the injury severity. The bicyclist is more likely to suffer more serious injuries in a crash accident when the bicyclist is male, or shorter. Electric bicycles have a smaller handlebar width. In general, the lower handlebar height is, or lower saddle height is, the more serious the bicyclist’s injury is. Safety training for drivers can help reduce the injury severity in crash accidents and improve traffic safety.

Factors impacting bike crash severity in urban areas

Introduction: Bicycling plays an important role as a major non-motorized travel mode in many urban areas. While increasingly serving as a key part of an integrated transportation demand management system and a sustainable mobility option, interest in biking as an active transportation mode has been unfortunately accompanied by an increase in the number of bike crashes, many with incapacitating injuries or fatal outcomes. Thus, to improve bicycling safety it is crucial to understand the critical factors that influence severe bicyclist crash outcomes, and to identify and prioritize policies and actions to mitigate these risks. Method: The study reported herein was conducted with this objective in mind. Our approach involves the use of classification models (logistic regression, decision tree and random forest), as well as techniques for treating unbalanced data by under sampling, oversampling, and weighted cost sensitivity (CS) learning, applied to bike crash data from the State of Tennessee’s two largest urban areas, Nashville and Memphis. Results: The results indicate that random forest with weighted CS offers the potential for greater explanatory accuracy, an important observation given the paucity of efforts to date in applying random forest to bike safety studies. Inadequate lighting conditions, crashes on roadways, speed limits, average annual daily traffic, number of lanes, and weekends are the critical features identified. Conclusion: Based on these results, a series of specific, suggested policy changes are presented for implementation consideration. Practical Applications: There is existing guidance in FHWA Lighting Handbook and TDOT’s Roadway Design Guidelines that spell out some engineering design solutions like lighting provisions, bicycle facility design, and traffic calming measures. These measures may alleviate the identified key features impacting fatal and incapacitating bicycle injuries. Further research should be conducted to gauge the efficacy of the solutions suggested.

Characteristics of Electric Scooter and Bicycle Injuries After Introduction of Electric Scooter Rentals in Oslo, Norway

When society introduces and accepts new transportation modes, it is important to map risks and benefits. To compare electric scooter (e-scooter) and bicycle injuries. This cohort study is based on prospectively collected data on Norwegian patients who sustained e-scooter or bicycle injuries and presented to an emergency department affiliated with Oslo University Hospital between January 1, 2019, and March 31, 2020. e-Scooter and bicycle injuries were evaluated for associations with sex, age, time of injury, helmet use, intoxication, body region, and injury severity. Descriptive statistics are presented as mean (SD) or number with percentage, with significance set at P < .05 (2-tailed). During the study period, 3191 patients were included (850 e-scooter riders, 2341 bicyclists) with 3839 injuries recorded (997 e-scooter, 2842 bicycle). The mean (SD) age of those injured was 34 (17) years, 2026 riders (63.5%) were male, 1474 (46.2%) were helmeted at the time of injury, and 516 (16.2%) were intoxicated by alcohol or other drugs. The annual incidence of injuries was 120 per 100 000 inhabitants for e-scooters and 340 per 100 000 inhabitants for bicycles. Men were overrepresented in both groups (529 e-scooter riders [62.2%] and 1497 bicyclists [63.9%]). e-Scooter riders were younger than bicyclists (mean [SD] age, 31 [12] vs 35 [18] years). Most injured e-scooter riders were aged 20 to 40 years, whereas injured bicyclists had a broader age distribution. e-Scooter injuries commonly occurred on weekends (378 [46.6%]) and during evening (230 [32.3%]) or nighttime (242 [34.1%]) hours. Most bicycle injuries occurred during weekdays (1586 [69.7%]) and daytime (1762 [61.3%]). e-Scooter riders were more often intoxicated (336 [39.5%] vs 180 [7.7%]) and had a lower rate of helmet use (18 [2.1%] vs 1456 [62.2%]). During nighttime, 230 injured e-scooter riders (91.3%) and 86 bicyclists (69.4%) were intoxicated. e-Scooter riders had more head and neck (317 [31.7%] vs 636 [22.4%]) and lower-limb (285 [28.6%] vs 632 [22.2%]) injuries and fewer upper-limb (341 [34.2%] vs 1276 [44.9%]), thoracic (41 [4.1%] vs 195 [6.9%]), and abdominal, pelvic, and lumbar (13 [1.3%] vs 103 [3.6%]) injuries. In this cohort study, e-scooter riders were younger than bicyclists, did not use helmets, were more often intoxicated, and were more often injured during nighttime. The rate of intoxication among e-scooter riders injured at night was high. Preventive measures, including awareness campaigns, regulating e-scooter availability, improving infrastructure, and implementing stricter helmet and alcohol policies, may prove effective for reducing injuries.

Bicycle Injuries Associated With Drug Use in the United States, 2019-2020.

The purpose of this study was to analyze the descriptive epidemiology of bicycle-related injuries associated with substances of abuse other than alcohol and to examine types of drugs involved and injury diagnoses. We used data from the 2019-2020 National Electronic Injury Surveillance System (NEISS) among persons seeking treatment in emergency departments in the United States. There were 11,314 bicycle injuries related to drug use. The most common drug reported was methamphetamine, followed by marijuana and opioids. The majority of injuries were fractures or internal organ injuries. Alcohol was involved in 22.3% of these incidents, and 32.3% of those injured were admitted to the hospital. Drug-related bicycle injuries are a significant public health issue. Prevention and control of these injuries is needed, but will likely prove to be difficult because of the variety of circumstances involved but will be aided by the expansion of quality surveillance data and further research examining the circumstances surrounding the injuries.

Comparative analysis of off-road vehicle crashes in children: motorcycles versus quad bikes

ObjectiveTo characterise and compare off-road motorcycle and quad bike crashes in children in New South Wales (NSW), Australia.MethodsA retrospective, cross-sectional study was performed of children aged 0–16 years, admitted to...

Growth Plan In Operational Design Management For Pt. Traffic Light Jaya

Traffic congestion is followed via way of means of more and more site visitors injuries. These are ruled via way of means of bike injuries, which additionally account for the very best part of fatalities and essential injuries. Careless riding and unruly conduct via way of means of drivers are the primary reasons of injuries in Indonesia. Penelitian ini bertujuan untuk a coordinated attempt which can also additionally alleviate a metropolis’s congestion is deemed necessary, specially in tackling busy and complicated intersections on the middle of the trouble to lessen losses. Research is carried out using qualitative research. It was concluded that The company must have good marketing and operational strategies to get more customers and maintain the business in order to keep it disrupting the times. The future challenge is very challenging for this business to set up a new strategy to meet market demands and globally transform. With the presence of a 24-hour hotline to receive customer complaints I feel is one form of responsibility from the company. Building a laboratory seems to be needed in the near future given the many demands in the three cities as a preliminary experiment. It is hoped that with good management operations, this business can dominate and be irreplaceable, given the purpose of establishing this company is to be a solution to the bottleneck and effectiveness of the government budget

Growth Plan In Operational Design Management For Pt. Traffic Light Jaya

Traffic congestion is followed via way of means of more and more site visitors injuries. These are ruled via way of means of bike injuries, which additionally account for the very best part of fatalities and essential injuries. Careless riding and unruly conduct via way of means of drivers are the primary reasons of injuries in Indonesia. Penelitian ini bertujuan untuk a coordinated attempt which can also additionally alleviate a metropolis’s congestion is deemed necessary, specially in tackling busy and complicated intersections on the middle of the trouble to lessen losses. Research is carried out using qualitative research. It was concluded that The company must have good marketing and operational strategies to get more customers and maintain the business in order to keep it disrupting the times. The future challenge is very challenging for this business to set up a new strategy to meet market demands and globally transform. With the presence of a 24-hour hotline to receive customer complaints I feel is one form of responsibility from the company. Building a laboratory seems to be needed in the near future given the many demands in the three cities as a preliminary experiment. It is hoped that with good management operations, this business can dominate and be irreplaceable, given the purpose of establishing this company is to be a solution to the bottleneck and effectiveness of the government budget

Bike Helmet Usage in the Most Disadvantaged Neighborhoods: A Focused Area for Trauma Prevention

IntroductionThere is a paucity of data to describe how neighborhood socioeconomic disadvantage (NSD) correlates with childhood injuries and outcomes. This study assesses the relationship of NSD to bicycle safety and trauma outcomes among pediatric bicycle versus automobile injuries. MethodsBetween 2008 and 2018, patients ≤18 y old with bicycle versus automobile injuries from a Level I pediatric trauma center were evaluated. Area Deprivation Index (ADI) was used to measure NSD. Patient demographics, injury, clinical data characteristics, and bike safety were analyzed. Traffic scene data from the Statewide Integrated Traffic Records System were matched to clinical records. Multivariate logistic regression was used to assess demographic characteristics related to helmet usage. ResultsAmong 321 patients, 84% were male with a median age of 12 y [interquartile range 9-13], and 44% were of Hispanic ethnicity. Hispanic ethnicity was greater in the most disadvantaged ADI groups (P < 0.001). Mortality occurred in two patients, and most (96%) were discharged home. Of Statewide Integrated Traffic Records System matched traffic records, 81% were at locations without a bike lane. No differences were found in GCS, intensive care unit admission, or length of stay by ADI. Hispanic ethnicity and the highest deprivation group were independently associated with lower odds of wearing a helmet (AOR 0.35, 95% confidence interval 0.1-0.9, P = 0.03; AOR 0.33 95% confidence interval 0.17-0.62; P = 0.001), while patient age and sex were unrelated to helmet usage. ConclusionsOutcomes for bike versus auto trauma remains similar across ADI groups. However, bike helmet usage is significantly lower among Hispanic children and those from neighborhoods with greater socioeconomic disadvantage.

Balance bike injuries: A retrospective chart review.

To determine the characteristics of balance bike injuries. This study retrospectively identified patients injured while riding a balance bike and treated at one paediatric emergency department in urban Japan, from February 2015 to July 2020. Data extracted were patient age and sex, circumstances and location of the injury occurrence, helmet use, adult supervision, injury type and body site, treatment and emergency department disposition. Based on these data and the balance bike manual instructions, cases were classified as correct and incorrect balance bike use. The study assessed 78 patients, age 2-6 years (median 3 years; 73% male). Of these 78 patients, 63 did not follow balance bike manual instructions (80.8%); 34 (43.6%) were injured while riding in prohibited places of use; 45 (57.7%) were injured in prohibited situations of use; and 37 (47.4%) were injured despite adult supervision. A total of 124 injuries were noted; the most common injured body site was the face (49 injuries, 39.5%), followed by the head (40 injuries, 32.3%). Most injuries were contusions and abrasions (65 injuries, 52.4%). More serious injuries were intensive care unit admission with intracerebral haemorrhage (1 patient) and lateral condyle fracture with surgical repair (1 patient). Most of the 78 balance bike injuries (76 patients) were mild; 2 were more severe and required intensive care unit admission or operation. Parents must be aware of the rules and instructions for balance bike use and ensure that their children can use the balance bike correctly.

Injuries and risk factors associated with bicycle and electric bike use in China: A systematic review and meta-analysis

Bicycles and electric bike riding have reshaped the transportation system worldwide and have significantly contributed to road traffic injuries. However, the risk factors associated with such injuries remain unclear. Therefore, this systematic review aimed to update and summarize the available evidence regarding risk factors that have been associated with bicycle and electric bike-related injuries in China. We searched for studies related to bicycles and electric bike-related injuries and deaths in China that were published in English from January 2000 to December 2020. In total, 18 studies met the inclusion criteria. Four domains of risk factors associated with such injuries were identified, including sociodemographic factors (e.g., age, sex, education level), riding behaviour (e.g., traffic rule violations, not wearing a helmet), vehicle and road factors (e.g., type of nonmotor vehicle and type of road), and temporal factors (such as commuting-hour riding or rush-hour riding). The pooled analysis showed that the mortality rate of e-bike injuries in China was 9.0% (95% confidence interval [CI]: 5.0%–13.0%). Understanding and identifying injury-related risk factors associated with bicycles and electric bike riding could provide important evidence to guide traffic safety management. More efforts are needed to reduce bicycle- and electric bike-related injuries in China. Targeted interventions, including enforcing helmet usage, requiring a driver’s licence to operate scooter-style electric bikes, and establishing a data collection and safe-riding credit system, may need to be developed. Moreover, given the global phenomenon of population ageing, targeted injury prevention interventions should be developed for senior riders, who are more likely to sustain severe bicycles and electric bike-related injuries and die due to injury.

Epidemiology, injury characteristics and clinical outcomes of bicycle and motorcycle accidents in the under 20 population: South Korea

BackgroundBicycles and motorcycles are a main means of transportation and leisure for individuals aged under 20 years in South Korea. We aimed to identify the epidemiology of injuries and describe and compare patterns of injury and clinical outcomes of two-wheel vehicle-related accidents in these individuals.MethodsWe analysed data obtained from the National Emergency Department Information System for 401 emergency departments (EDs) from January 2016 to December 2018. We included patients aged < 20 years who experienced injuries while driving or a passenger on two-wheeled vehicles. We analysed patients with a bicycle-related injury and those with a motorcycle-related injury, and then compared two groups and performed a regression analysis for factors predicting severe trauma.ResultsThis study enrolled 54,342 two-wheel vehicle injury patients (37,410 bicycle and 16,932 motorcycle-related), of which, 86.8% (bicycle) and 94.9% (motorcycle) were males. External injuries were the most common. ED mortality was 9 (0.0%) for bicycles and 53 (0.3%) for motorcycles.Overall, 3,346 (8.9%) patients were hospitalised with bicycle injuries and 4,096 (24.2%) with motorcycle injuries. Among admitted patients with bicycle-related injuries, 48.7% had upper extremity injuries and among those admitted patients with motorcycle-related injuries, 76.0% had lower extremity injuries.Among hospitalised patients, the mean injury severity score (ISS) was 12.0 ± 12.6 in bicycle-related injury and 17.6 ± 15.4 in motorcycle-related injury. The number of patients with ISS ≥ 16 was 27.6% for bicycle related injuries and 45.2% for motorcycle-related injuries. The mean length of hospital stay was 191.5.8 ± 224.2 h for bicycle injury, and 359.6 ± 416.7 h for motorcycles. Hospital mortality cases were 0.2% with bicycle injury and 1.2% with motorcycle injury. Motorcycle-related injuries had more severe injury (ISS ≥ 16), with an adjusted odds ratio of 2.825 (95% confidence interval 2.610–3.059) compared to bicycle-related injuries.ConclusionsIn the population aged under 20 years, two-wheel vehicle-related occurred predominantly in males. When using two-wheeled vehicles, motorcycle injuries were higher in patients aged over 14 years and were associated with higher ISS (≥ 16). Political efforts should be made to educate under 20 years of age for safe driving and to wear protective gear, including helmets to prevent severe injury.

Epidemiology of traumatic cervical spinal fractures in a general Norwegian population

BackgroundIn Western countries, the typical cervical spine fracture (CS-Fx) patient has historically been a young male injured in a road traffic accident. Recent reports and daily clinical practice clearly indicate a change in the typical patient from a young male to an elderly male or female with comorbidities. This study aimed to establish contemporary population-based epidemiological data of traumatic CS-Fx for use in health-care planning and injury prevention.MethodsThis is a population-based retrospective database study (with prospectively collected data) from the Southeast Norway health region with 3.0 million inhabitants. We included all consecutive cases diagnosed with a CS-Fx between 2015 and 2019. Information regarding demographics, preinjury comorbidities, trauma mechanisms, injury description, treatment, and level of hospital admittance is presented.ResultsWe registered 2153 consecutive cases with CS-Fx during a 5-year period, with an overall crude incidence of CS-Fx of 14.9/100,000 person-years. Age-adjusted incidences using the standard population for Europe and the World was 15.6/100,000 person-years and 10.4/100,000 person-years, respectively. The median patient age was 62 years, 68% were males, 37% had a preinjury severe systemic disease, 16% were under the influence of ethanol, 53% had multiple trauma, and 12% had concomitant cervical spinal cord injury (incomplete in 85% and complete in 15%). The most common trauma mechanisms were falls (57%), followed by bicycle injuries (12%), and four-wheel motorized vehicle accidents (10%). The most common upper CS-Fx was C2 odontoid Fx, while the most common subaxial Fx was facet joint Fx involving cervical level C6/C7. Treatment was external immobilization with a stiff neck collar alone in 65%, open surgical fixation in 26% (giving a 3.7/100,000 person-years surgery rate), and no stabilization in 9%. The overall 90-day mortality was 153/2153 (7.1%).ConclusionsThis study provides an overview of the extent of the issue and patient complexity necessary for planning the health-care management and injury prevention of CS-Fx. The typical CS-Fx patient was an elderly male or female with significant comorbidities injured in a low-energy trauma. The overall crude incidences of CS-Fx and surgical fixation of CS-Fx in Southeast Norway were 14.9/100,000 person-years and 3.7/100,000 person-years, respectively.

The characteristics, management and outcomes of high- and low-grade renal injuries in paediatric trauma patients at a major trauma centre

Introduction Children, given anatomical variations, are at increased risk of renal injury following trauma. The management of paediatric renal injuries has, similar to other solid organ injuries, largely shifted towards conservative management; however, hemodynamically unstable patients may still warrant surgical exploration or interventional techniques. The aim of this study is to describe the local incidence, demographics, morbidity and outcomes associated with high- and low-grade renal injury in a paediatric major trauma population. Method This was a 5-year retrospective review of trauma registry data and chart analysis of all paediatric renal injuries from major trauma at a North American level 1 paediatric trauma centre between January 2016–31 December 2020. Data was analysed using SPSS v27 with p &lt; 0.05 considered significant. Results Of 1334 major trauma patients, 45 suffered a kidney injury (20 high-grade and 25 low-grade injuries), of which 93.3% underwent conservative management with no difference in outcomes between groups. 80% of patients had concurrent injuries (a quarter requiring surgery for these), with a trend towards higher rates of chest injuries in high-grade renal injury patients ( p = 0.08). Bicycle injuries were statistically more likely to cause high-grade renal injury ( p = 0.02). Angiography was utilized infrequently (3/45 patients, 6.6%), and no patients underwent embolization in our study population. Overall mortality (4.4%) and length of stay were unaffected by grade of injury. Conclusion Paediatric renal injury is an uncommon injury in major trauma patients (3.4%). Most cases can be managed conservatively regardless of the grade of injury. Renal injury patients are likely to have concurrent injuries, often requiring surgery. Further studies are needed to measure the success and utilization of interventional radiology techniques for management in children.

Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes

The behavioral pathways in traffic crashes describe the chained linkages among contributing factors, pre-crash road user behaviors, and crash outcomes. Bicyclists are more vulnerable than motorists on road and their pre-crash behaviors play an essential role in the pathways leading to injuries. The objective of this study is to develop a methodological framework that integrates machine learning with path analysis to quantify behavioral pathways in bicycle-motor vehicle crashes. Specifically, two sets of models are developed for predicting: 1) pre-crash behaviors given contributing factors and 2) bicyclist injury severity given contributing factors including pre-crash behaviors. The path analysis chains machine learning models to establish the indirect linkages between contributing factors and injury severities through correlates of pre-crash behaviors. This study explored five machine learning methods, including Random Forest (RF), Categorical Naive Bayes (CNB), Support vector machine (SVM), AdaBoost (Boost), and Neural network (NN). To reduce the bias of any single model, this study proposes a technique to combine model estimates by averaging marginal effects. This study used a dataset containing 9,296 bicycle-motor vehicle crashes to demonstrate the application of the framework. Across five machine learning models, the signs of marginal effects generally agree but their magnitudes vary substantially. The pre-crash behavior of “bicyclist failed to yield” increases bicyclist injury severity by 1.11%. The path analysis results highlighted contributing factors related to risky pre-crash behaviors that lead to severe injuries, such as bicyclist intoxication. The framework is expected to support agencies’ decision-making to improve cycling safety by reducing unsafe behaviors on roads.

Bicyclists injury severities: An empirical assessment of temporal stability

Cyclists are among the most vulnerable participants in road traffic, making their safety a top priority. Riding behavior of bicyclists could shift over time, affecting the level of injuries sustained in bicyclist-involved crashes. Many studies have been done to identify the factors influencing bicyclist injury severity, but the temporal stability of these variables over time needs further study. The temporal instability of components that affect the cyclist injury levels in bicycle collisions is explored in this paper. To obtain potential unobserved heterogeneity, yearly models of cyclist-injury levels (including potential consequences of no, minor, and severe injury) were measured separately applying a random parameters logit model that allows for potential heterogeneity in estimated parameters’ means and variances. Employing a data source on bicycle collisions in Los Angeles, California, over the course of six years (January 1, 2012 to December 31, 2017), several variables which may impact the injury level of cyclists were explored. This paper has also employed a set of likelihood ratio tests assessing the temporal instability of the models. The temporal instability of the explanatory parameters has been evaluated with marginal effects. The results of the model assessment indicate that several factors may raise the chances of severe bicyclist injuries in collisions, including cyclists older than 55 years old, cyclists who were identified to be at-fault in crashes, rear-end collisions, cyclists who crossed into opposing lane before the collision, crashes occurring early mornings (i.e., 00:00 to 06:00) and so on. The results also showed that the details and estimated parameters of the model do not remain stable over the years, however the source of this instability is unclear. In addition, the findings of model estimation demonstrate that considering the heterogeneity in the random parameter means and variances will enhance the overall model fit. This study also emphasizes the significance of accounting for the transferability of estimated models and the temporal instability of parameters influencing the injury severity outcomes in order to dynamically examine the collected data and adjust safety regulations according to new observations.

Analyzing pediatric bicycle injuries using geo-demographic data

PurposeBicycle accidents are potentially preventable, and helmets can mitigate the severity of injuries. The purpose of the study it to investigate geo-demographic areas to establish prevention policies and targeted programs. MethodsFrom October 2013 to March 2020 all bicycle injuries at a Level 1 trauma center were collected for ages ≤18 years. Demographics, injuries, and outcomes were analyzed. Incidents were aggregated to zip codes and the Local Indicators of Spatial Association (LISA) statistic was used to test for spatial clustering of injury rates per 10,000 children. ResultsOver the 8-year time period, 77 cases were identified with an average age of 13±4 years, 83% male and 48% non-Hispanic white. The majority of patients (98%) were not wearing a helmet. Loss of consciousness was reported in 44% and 21% sustained a traumatic brain injury. Twenty-eight percent required ICU care and 36% required operative interventions. There was only 1 mortality in the cohort (<1%).Injuries were more common in lower household income zip codes (Figure 1). Six zip codes encompassing several interstate exits and the connected heavy-traffic roadways comprise a statistically significant cluster of pediatric bicycle accidents (Figure 1). ConclusionLow-income neighborhoods and those near major roadways held the highest risk for pediatric bicycle accidents. Use of helmets was extremely low in the patient population, with high rates of traumatic brain injury. With this information, targeted programs to address high-risk intersections, helmet access, and safety education can be implemented locally.

E-scooter, e-bike and bicycle injuries in the same period-Aprospective analysis of alevel1 trauma center

HintergrundDie tatsächliche Anzahl verunglückter E‑Scooter-Fahrer in Deutschland scheint deutlich höher zu sein, als es die aktuellen Zahlen des Statistischen Bundesamtes vermuten lassen. Diese epidemiologische Studie untersucht E‑Scooter-Verletzungen und vergleicht diese mit E‑Bike- und Fahrradverletzungen.FragestellungZur Schaffung einer vergleichbaren Datenlage über die Gefahren von E‑Scootern, E‑Bikes und Fahrrädern wurden die typischen Verletzungsmuster analysiert und daraus Präventionsmöglichkeiten abgeleitet.Material und MethodeEs wurden alle Patienten, die sich nach Unfällen mit Beteiligung von E‑Scootern, E‑Bikes und Fahrrädern über die universitäre Notaufnahme eines Level-1-Traumazentrums im Zeitraum vom 15. Juni 2019 bis 31. Oktober 2020 vorstellten, prospektiv untersucht.ErgebnisseEs wurden 68 verunglückte E‑Scooter-Fahrer erfasst. Davon waren signifikant mehr männlich als weiblich (p = 0,032). Das mittlere Alter betrug 31,1 (±13) Jahre, und lediglich 11,8 % (n = 8) der Unfälle wurden polizeilich registriert. Im gleichen Zeitraum wurden 34 verunglückte E‑Bike- und 356 Fahrradfahrer erfasst. In allen 3 Gruppen waren die meisten Verletzungen im Kopfbereich, gefolgt von Verletzungen an den oberen Extremitäten. Signifikant mehr E‑Scooter-Fahrer hatten einen ISS ≥ 16 als in der Gruppe der verunglückten Fahrradfahrer (p = 0,016). Verunglückte E‑Scooter-Fahrer hatten eine signifikant längere Krankenhausverweildauer (KHVD) als E‑Bike-Fahrer (p = 0,003) und als Fahrradfahrer (p = 0,001). Einen Helm trugen 52,9 % (n = 18) der E‑Bike- und 53,3 % (n = 113) der Fahrradfahrer, dagegen nur 1,5 % (n = 1) der E‑Scooter-Fahrer. Die häufigste Unfallursache bei E‑Bike- (17,7 %; n = 6) und Fahrradfahrern (10,4 %; n = 37) war das Wegrutschen auf Straßenbahnschienen, bei E‑Scooter-Fahrern die Kollision mit einem Bordstein (7,4 %; n = 5).DiskussionDie 3 untersuchten Patientenkollektive zeigten differierende Verletzungsursachen und -profile. Als Ursachen für einen erhöhten Anteil von Schwerverletzten im Vergleich zu Fahrradfahrern sind die Elektromobilität, Fahren unter Alkoholeinfluss und das unzureichende Tragen eines Helms bei E‑Scootern bei Dominanz von Kopfverletzungen zu nennen. Da 73,5 % (n = 50) der erfassten E‑Scooter-Unfälle nicht polizeilich registriert wurden, ist von einer sehr viel höheren Anzahl von E‑Scooter-Unfällen auszugehen als bisher angenommen. Präventive Maßnahmen könnten die Einführung einer Helmpflicht, eine höhere Anzahl von Verkehrskontrollen, Fahrsicherheitstrainings und der Ausbau von Fahrradwegen sein.

Traffic and Pedestrian Safety, Pre and Post Covid-19

The effects of planning initiatives aimed at increasing bicycle use have been little studied in relation to decreased auto use, injuries and pollution. As most programs promote the idea those public investments in bicycle lanes, traffic calming engineering of streets and valuing bicycling as economic and environmentally sound, this article assesses the goals and results. While bicycle use for commuting has decreased in recent years and congestion increased, focus on the effects of engineering streets for calming and bike and pedestrian use were found often to be associated with increased driving times, increased pedestrian and bicyclist injuries and increased pollution. While the economic slowdown due to Covid-19 has reduced traffic, use of existing traffic design has produced intensified conflicts which will be continuing patterns when full economic recovery takes place. The article makes use of data from a number of countries and original fieldwork.