Modelling the effect on injuries and fatalities when changing mode of transport from car to bicycle

Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Cycling is a sustainable and healthy mode of transportation with direct links to reducing traffic congestion, lowering greenhouse gas emissions, and improving air quality. However, from a safety perspective, bicyclists represent a risky road user group with a higher likelihood of sustaining severe injuries when involved in vehicle crashes. With various determinants known to affect bicyclist injury severity and vary across locations, this study investigates the factors affecting bicyclist injury severity and temporal instability, considering the location of crashes. More specifically, the objective of this study is to understand differences in injury severities of intersection and non-intersection-related single-bicycle-vehicle crashes using four year crash data from the state of Florida. Random parameters logit models with heterogeneity in the means and variances are developed to model bicyclist injury severity outcomes (no injury, minor injury, and severe injury) for intersection and non-intersection crashes. Several variables affecting injury severities are considered in model estimation, including weather, roadway, vehicle, driver, and bicyclist characteristics. The temporal stability of the model parameters is assessed for different locations and years using a series of likelihood ratio tests. Results indicate that the determinants of bicyclist injury severities change over time and location, resulting in different injury severities of bicyclists, with non-intersection crashes consistently resulting in more severe bicyclist injuries. Using a simulation-based out-of-sample approach, predictions are made to understand the benefits of replicating driving behaviour and facilities similar to intersections for non-intersection locations, which could benefit in reducing bicyclist injury severity probabilities.

Injury severity prediction of cyclist crashes using random forests and random parameters logit models

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

Systematic literature review of 10 years of cyclist safety research

Cyclist safety is a research field that is gaining increasing interest and attention, but still offers questions and challenges open to the scientific community. The aim of this study was to provide an exhaustive review of scientific publications in the cyclist safety field. For this purpose, Bibliometrix-R tool was used to analyse 1066 documents retrieved from Web of Science (WoS) between 2012 and 2021. The study examined published sources and productive scholars by exposing their most influential contributions, presented institutions and countries most contributing to cyclist safety and explored countries open towards international collaborations. A keywords analysis provided the most frequent author keywords in cyclist safety shown in a word cloud with E-bike, behaviour, and crash severity representing the primary keywords. Furthermore, a thematic map of cyclist safety field drafted from the author's keywords was identified. The strategic diagram is divided in four quadrants and, according to both density and centrality, the themes can be classified as follows: 1) motor themes, characterized by high value of both centrality and density; 2) niche themes, defined by high density and low centrality; 3) emerging or declining themes, featured by low value of both centrality and density; and 4) basic themes, distinguished by high centrality and low density. The motor themes (i.e., the main topics in cyclist safety field) crash severity and bike network were further explored. The research findings will be useful to develop strategies for making bike a safer and more confident form of transport as well as to guide researchers towards the future scientific knowledge.

Bicycle-related cervical spine injuries

•

The incidence of bicycle-related cervical spine injuries (CSI) was 1.7/100,000/year.

•

Bicycling was the second most common cause of CSI, only preceded by falls.

•

Occipital condyle fracture was common in bicyclists.

•

Bicyclists with CSI were associated with more multiple trauma and concomitant head injury than non-bicyclists.

Background

Bicyclists are vulnerable road users. The aim of this paper was to describe all bicycle-related traumatic cervical spine injuries (CSIs) in the South-East region of Norway (2015–2019), and to investigate whether certain types of CSIs are typical for bicyclists.

Methods

Retrospective cohort study of prospectively collected registry data of all CSIs in the South-East region of Norway (3.0 million inhabitants), from 2015 to 2019. Patient characteristics, injury types, and treatment were summarized with descriptive statistics. Bayesian multivariable logistic regression was used to identify potential factors associated with occipital condyle fractures (OC-Fx) or odontoid fractures (OFx).

Results

During the five-year study period, 2,162 patients with CSIs were registered, and 261 (12%) were bicycle-related. The incidence of bicycle-related CSIs was 1.7/100,000 person-years. The median age of the patients with bicycle-related CSIs was 55 (IQR: 22) years, 83% were male, 71% used a helmet, 16% were influenced by ethanol, 12% had a concomitant cervical spinal cord injury (SCI), and 64% sustained multiple traumas. The three most common bicycle-related CSIs were C6/C7 fracture (Fx) (28%), occipital condyle Fx (OC-Fx) (23%) and C5/C6 Fx (19%). Patients with bicycle-related CSIs compared to patients with non-bicycle related CSIs were younger, more often male, had fewer comorbidities, more likely multiple traumas, more often had OC-Fx, and less often sustained an odontoid fracture (OFx). Multivariable logistic regression of potential risk factors for OC-Fx demonstrated a significantly increased risk of OC-Fx for bicyclists compared to non-bicyclists (OR=2.8).The primary treatment for bicycle-related CSIs was external immobilization in 187/261 (71.6%) cases, open surgical fixation in 44/261 (16.8%), and no treatment in 30/261 (11.5%).

Conclusion

Bicycle crashes are a frequent cause of CSIs in the Norwegian population and should be of concern to the public society. The three most common bicycle-related CSIs were C6/C7 fracture, occipital condyle fracture and C5/C6 fracture.

Epidemiology of traumatic cervical spinal fractures in a general Norwegian population

BACKGROUND

In 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.

METHODS

This 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.

RESULTS

We 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%).

CONCLUSIONS

This 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.

Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle-bicycle crashes

INTRODUCTION

In recent years, Australia is seeing an increase in the total number of cyclists. However, the rise of serious injuries and fatalities to cyclists has been a major concern. Understanding the factors affecting the fatalities and injuries of bicyclists in crashes with motor vehicles is important to develop effective policy measures aimed at improving the safety of bicyclists. This study aims to identify the factors affecting motor vehicle-bicycle (MVB) crashes in Victoria, Australia and introducing effective countermeasures for the identified risk factors.

METHOD

A data set of 14,759 MVB crash records from Victoria, Australia between 2006 and 2019 was analyzed using the binary logit model and latent class clustering.

RESULTS

It was observed that the factors that increase the risk of fatalities and serious injuries of bicyclists (FSI) in all clusters are: elderly bicyclist, not using a helmet, and darkness condition. Likewise, in areas with no traffic control, clear weather, and dry surface condition (cluster 1), high speed limits increase the risk of FSI, but the occurrence of MVB crashes in cross intersection and T-intersection has been significantly associated with a reduction in the risk of FSI. In areas with traffic control and unfavorable weather conditions (cluster 2), wet road surface increases the risk of FSI, but the areas with give way sign and pedestrian crossing signs reduce the risk of FSI. Practical Applications: Recommendations to reduce the risk of fatalities or serious injury to bicyclists are: improvement of road lighting and more exposure of bicyclists using reflective clothing and reflectors, separation of the bicycle and vehicle path in mid blocks especially in high-speed areas, using a more stable bicycle for the older people, monitoring helmet use, improving autonomous emergency braking, and using bicyclist detection technology for vehicles.

Overall health impacts of a potential increase in cycle commuting in Stockholm, Sweden

AIMS

To estimate the overall health impact of transferring commuting trips from car to bicycle.

METHODS

In this study registry information on the location of home and work for residents in Stockholm County was used to obtain the shortest travel route on a network of bicycle paths and roads. Current modes of travel to work were based on travel survey data. The relation between duration of cycling and distance cycled was established as a basis for selecting the number of individuals that normally would drive a car to work, but have a distance to work that they could bicycle within 30 minutes. The change in traffic flows was estimated by a transport model (LuTrans) and effects on road traffic injuries and fatalities were estimated by using national hospital injury data. Effects on air pollution concentrations were modelled using dispersion models.

RESULTS

Within the scenario, 111,000 commuters would shift from car to bicycle. On average the increased physical activity reduced the one-year mortality risk by 12% among the additional bicyclists. Including the number of years lost due to morbidity, the total number of disability adjusted life-years gained was 696. The amount of disability adjusted life-years gained in the general population due to reduced air pollution exposure was 471. The number of disability adjusted life-years lost by traffic injuries was 176. Also including air pollution effects among bicyclists, the net benefit was 939 disability adjusted life-years per year.

CONCLUSIONS

Large health benefits were estimated by transferring commuting by car to bicycle.

The Health Cost of Transport in Cities

Purpose of Review

The study aims to provide an understanding of health cost assessments of different transport modes in urban contexts, and their relevance for transport planning and political decision-making.

Recent Findings

There is strong evidence that motorized transportation imposes a high health cost on society, and specifically children. In contrast, active transport is a very significant health benefit.

Summary

Economic analyses support urban change in favor of compact neighborhoods and public transit, as well as infrastructure exclusively devoted to active transport. Private cars need to be restricted because of the high cost they impose on society.

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

INTRODUCTION

Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019).

METHODS

This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns.

RESULTS

Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters.

CONCLUSIONS

The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.

A New Assessment of Bicycle Helmets: The Brain Injury Mitigation Effects of New Technologies in Oblique Impacts

New helmet technologies have been developed to improve the mitigation of traumatic brain injury (TBI) in bicycle accidents. However, their effectiveness under oblique impacts, which produce more strains in the brain in comparison with vertical impacts adopted by helmet standards, is still unclear. Here we used a new method to assess the brain injury prevention effects of 27 bicycle helmets in oblique impacts, including helmets fitted with a friction-reducing layer (MIPS), a shearing pad (SPIN), a wavy cellular liner (WaveCel), an airbag helmet (Hövding) and a number of conventional helmets. We tested whether helmets fitted with the new technologies can provide better brain protection than conventional helmets. Each helmeted headform was dropped onto a 45° inclined anvil at 6.3 m/s at three locations, with each impact location producing a dominant head rotation about one anatomical axes of the head. A detailed computational model of TBI was used to determine strain distribution across the brain and in key anatomical regions, the corpus callosum and sulci. Our results show that, in comparison with conventional helmets, the majority of helmets incorporating new technologies significantly reduced peak rotational acceleration and velocity and maximal strain in corpus callosum and sulci. Only one helmet with MIPS significantly increased strain in the corpus collosum. The helmets fitted with MIPS and WaveCel were more effective in reducing strain in impacts producing sagittal rotations and a helmet fitted with SPIN in coronal rotations. The airbag helmet was effective in reducing brain strain in all impacts, however, peak rotational velocity and brain strain heavily depended on the analysis time. These results suggest that incorporating different impact locations in future oblique impact test methods and designing helmet technologies for the mitigation of head rotation in different planes are key to reducing brain injuries in bicycle accidents.

The number of patients hospitalized with bicycle injuries is increasing - A cry for better road safety

INTRODUCTION

Norwegian authorities encourage people to commute by bicycle to improve public health, decrease rush-hour traffic jams and reduce pollution. However, increasing the number of bicyclists, especially in the rush-hour traffic, may increase the number of serious bicycle injuries.

OBJECTIVE

To explore trends in hospitalized bicycle injuries at a Norwegian level I trauma centre during the last decade.

METHODS

Data was extracted from the prospectively registered institutional trauma registry. We identified patients admitted after bicycle injuries between 2005 and 2016.

RESULTS

A total of 1543 patients were identified. Median age was 40 years (range 3-91) and 73 % were males. The majority of weekday injuries occurred in the morning and during the afternoon rush-hour, peaking at 8 am. and 4 pm. The annual number of admitted bicycle injuries increased from 79 to 184 during the study period. Also, an increase in the share of bicyclists using helmets was observed. The median Injury Severity Score (ISS) of 10 remained unchanged. 63 % had serious trauma (ISS ≥ 9), while 34 % suffered severe trauma (ISS ≥ 16). The absolute number of both serious and severe trauma increased annually. 36 % had head and neck injuries, while 16 % had chest injuries graded with Abbreviated Injury Scale ≥3. Loss of consciousness with Glasgow Coma Scale score <9 was seen in 7%. Median length of hospitalization was 3 days, and 39 % had surgery in one or more body regions. The 30-day mortality was 2.3 %.

CONCLUSIONS

The number of admitted bicycle injuries to our trauma centre is increasing. Rush-hour injuries dominate during weekdays. Bicycle injuries can be devastating and deserve more public attention to promote road safety.

Bicycle-Vehicle Conflict Risk Based on Cyclist Perceptions: Misestimations of Various Risk Factors

Cycling is a sustainable but vulnerable mode of transportation. Intersections’ bicycle-vehicle crashes are particularly dangerous. This paper explores the discordance between empirical evidence and cyclists’ perceptions of the various risk factors of cycling. Ridge regression was adopted to identify risk factors from bicycle-vehicle conflict data. A questionnaire was distributed to assess cyclists’ perceptions of safety and danger over the same candidate risk factors. There was indeed discordance between the data and the questionnaire results. Cyclists appear to misestimate risk in certain factors such as bus stops and subway stations. Understanding these misestimations can provide a foundation for safety improvements and for promoting cycling as a sustainable mode of transportation.

Sequence analysis of sickness absence and disability pension in the year before and the three years following a bicycle crash; a nationwide longitudinal cohort study of 6353 injured individuals

Background

Bicyclists are the road user group with the highest number of severe injuries in the EU, yet little is known about sickness absence (SA) and disability pension (DP) following such injuries.

Aims

To explore long-term patterns of SA and DP among injured bicyclists, and to identify characteristics associated with the specific patterns.

Methods

A longitudinal register-based study was conducted, including all 6353 individuals aged 18–59 years and living in Sweden in 2009, who in 2010 had incident in-patient or specialized out-patient healthcare after a bicycle crash. Information about sociodemographic factors, the injury, SA (SA spells > 14 days), and DP was obtained from nationwide registers. Weekly SA/DP states over 1 year before through 3 years after the crash date were used in sequence and cluster analyses. Multinomial logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) for factors associated with each identified sequence cluster.

Results

Seven clusters were identified: “No SA or DP” (58.2% of the cohort), “Low SA or DP” (7.4%), “Immediate SA” (20.3%), “Episodic SA” (5.9%), “Long-term SA” (1.7%), “Ongoing part-time DP” (1.7%), and “Ongoing full-time DP” (4.8%). Compared to the cluster “No SA or DP”, all other clusters had higher ORs for women, and higher age. All clusters but “Low SA and DP” had higher ORs for inpatient healthcare. The cluster “Immediate SA” had a higher OR for: fractures (OR 4.3; CI 3.5–5.2), dislocation (2.8; 2.0–3.9), sprains and strains (2.0; 1.5–2.7), and internal injuries (3.0; 1.3–6.7) compared with external injuries. The cluster “Episodic SA” had higher ORs for: traumatic brain injury, not concussion (4.2; 1.1–16.1), spine and back (4.5; 2.2–9.5), torso (2.5; 1.4–4.3), upper extremities (2.9; 1.9–4.5), and lower extremities (3.5; 2.2–5.5) compared with injuries to the head, face, and neck (not traumatic brain injuries). The cluster “Long-term SA” had higher ORs for collisions with motor vehicles (1.9;1.1–3.2) and traumatic brain injury, not concussion (18.4;2.2–155.2).

Conclusion

Sequence analysis enabled exploration of the large heterogeneity of SA and DP following a bicycle crash. More knowledge is needed on how to prevent bicycle crashes and especially those crashes/injuries leading to long-term consequences.

Health status and quality of life among road users with permanent medical impairment several years after the crash

OBJECTIVE

Improvements in road infrastructure and vehicle safety have been achieved in many countries during the last decades. As the number of fatalities have dropped, the consequences of non-fatal injuries have been brought into focus. Therefore, the objective was to investigate self-reported health status and health-related quality of life several years after the crash for road-users that sustained injuries resulting in permanent medical impairment (PMI).

METHODS

A self-administered questionnaire using instruments to measure if health, health-related quality of life and physical activity had been affected by the crash, were used. The injured road-users were identified from insurance policy holders of the Folksam Insurance Group. The response rate was 29%, a total of 2078 responses were received from the 7174 road-users with PMI that received the questionnaire.

RESULTS

In total 85% were still suffering from the injuries several years after the crash (8-18 year after the crash). Furthermore, road-users with injuries to the spine were having highest pain intensity. Older road-users had poorer self-reported health status than younger road-users. Although, younger road-users had the greatest change in physical activity when comparing before and after the crash. Before the crash in total 63% were physically active while only 34% after the crash. The higher the PMI the higher it affected health several years after the crash.

CONCLUSIONS

The Swedish definition of serious injury, an injury leading to PMI, was found to correlate with self-reported health loss; 85% of the injured road-users reported that they still had remaining symptoms several years after the accident. The injured body region leading to PMI after an accident can vary from the body regions reported to cause long-term health loss. It was found that the higher the degree of PMI the higher the health loss. Sustaining a PMI regardless severity and injured body region has the same effects on general health for men and women. Sustaining a PMI will both lower the health-related quality of life and physical activity after the crash compared to before.

Applying fast and frugal tree heuristic algorithm to identify factors influencing crash severity of bicycle-vehicle crashes in Tamilnadu

Though bicycle as a mode of transport has many environmental and societal benefits as well as health benefits, bicyclists are one of the most vulnerable road users. According to the report by the Ministry of Road Transport and Highways (MoRTH, 2017), there is a sharp increase in the number of fatal victims in respect of bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2585 in 2016 to 3559 in 2017, a 37.7% increase. In the present study, we present the analysis of the effect of the crash, geometric, environmental and cyclist characteristics on the bicycle-vehicle involved collisions by using the crash dataset of nine years (2009-2017) from Tamilnadu RADMS (Road Accident Data Management System) database with the application of fast and frugal tree (FFT) heuristic algorithm. The complete dataset (9978 crashes) was divided into two separate datasets: training data (6984 crashes) for the development of model and testing data (2984 crashes) for the performance evaluation. FFT algorithm identifies five major hues or variable attributes that influence the severity of bicycle crashes. The five major hues include the number of lanes, road separation, intersection, colliding vehicle type and road category. From the results of the present study, FFT acts as a complementary tool to other complex machine learning algorithms such as support vector machines, random forest, logistic regression and CART. The findings of the present study provide important insights for reducing the severity of bicycle-involved crashes at the planning and operations levels.

The characteristics of dockless electric rental scooter-related injuries in a large U.S. city

OBJECTIVE

To describe the characteristics of dockless electric rental scooter ("e-scooter")-related injuries presenting to two emergency departments in one large U.S. city.

METHODS

This observational cohort study utilized the city's public health syndromic surveillance system to prospectively identify patients with e-scooter-related injuries presenting between September and November 2018. The medical records for all adult patients treated at the two participating emergency departments were manually reviewed to extract demographic and clinical data. Cases involving mobility scooters or non-electric scooters were excluded.

RESULTS

For the 124 included adult patients with e-scooter-related injuries, the median age was 30 years (IQR: 22-43), they were predominantly male (59.7%), and approximately half (51.6%) arrived by ambulance. Falling from the scooter (84.7%) was the most common mechanism; twelve patients (9.7%) had collided with a motor vehicle. Head and face injuries (45.5%) were common; only 2 patients (1.6%) were documented as wearing a helmet at the time of injury. Most patients (n = 112, 90.3%) required imaging, more than half (n = 78, 62.9%) required an emergency department procedure, and 26 (21.0%) required surgical intervention. Most patients were discharged home, but 35 (28.2%) were admitted to hospital. Two patients (1.6%) were admitted to the intensive care unit.

CONCLUSIONS

E-scooters are an emerging transportation technology associated with a wide range of potentially serious injuries that consume substantial emergency department and hospital resources. Head injuries are a particular concern, as few e-scooter riders are wearing helmets at the time of injury.

The potential of different countermeasures to prevent injuries with high risk of health loss among bicyclists in Sweden

Objective: As bicyclists account for the largest share of serious injuries in Sweden, focus to improve safety for bicyclists is needed. While knowledge about fatal bicycle crashes is rather extensive, the number of studies that have investigated non-fatal injuries is still rather limited. The aim of this study was to estimate the potential of different countermeasures to reduce crashes resulting in injuries with high risk of health-loss among cyclists in Sweden. A further aim was to describe the residual-that is, crashes that were not considered to be addressed by the analyzed countermeasures.Methods: A sample of individuals with specific injury diagnoses was drawn from the Swedish national crash database Strada. A survey form was used to collect additional information about the crash and the health-related outcomes. The potential of countermeasures currently included in the Swedish Safety Performance Indicators, as well as of countermeasures that could be described as "existing but not fully implemented" was assessed. The overall potential of all countermeasures assessed was calculated, giving a grand total without double counting. Cases that were considered not to be addressed by any of the countermeasures included (i.e., the residual crashes) were described in more detail.Results: The current Swedish Safety Performance Indicators that relate to safe cycling addressed 22% of crashes. Improved maintenance by deicing and removal of snow from bicycle infrastructure was found to have the highest potential (8%), followed by improved crashworthiness of passenger cars (5%) and safer bicycle crossings (4%). The potential for existing but not fully implemented safety improvements was 56%. The greatest potential was found for Autonomous Emergency Braking with cyclist detection for passenger cars (12%), followed by studded winter tyres for bicycles (12%), and improved maintenance on non-bicycle infrastructure (11%). In total, taking double counting into consideration, all safety improvements could address 64% of all crashes. Among the residual crashes, the majority (69%) were single bicycle crashes of which most were related to wheel locking during braking and losing balance at low speed or stationary.Conclusions: Compared with fatal crashes that involve a majority of bicycle-car crashes, the crashes leading to health-loss are mostly single bicycle crashes. Therefore, innovation and development of additional countermeasures to improve safety for bicyclists should focus on single bicycle crashes.

Exploring the severity of bicycle-vehicle crashes using latent class clustering approach in India

INTRODUCTION

Bicyclists are vulnerable users in the shared asset like roadways. However, people still prefer to use bicycles for environmental, societal, and health benefits. In India, the bicycle plays a role in supporting the mobility to more people at lower cost and are often associated with the urban poor. Bicyclists represents one of the road user categories with highest risk of injuries and fatalities. According to the report by the Ministry of Road Transport and Highways (Accidents, 2017) in India, there is a sharp increase in the number of fatal victims for bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2,585 in 2016 to 3,559 in 2017, a 37.7% increase.

METHOD

Few studies have only investigated the crash risk perceived by the bicyclists while interacting with other road users. The present paper investigates the injury severity of bicyclists in bicycle-vehicle crashes that occurred in the state of Tamilnadu, India during the nine year period (2009-2017). The analyses demonstrate that dividing bicycle-vehicle collision data into five clusters helps in reducing the systematic heterogeneity present in the data and identify the hidden relationship between the injury severity levels of bicyclists and cyclists demographics, vehicle, environmental, temporal cause for the crashes.

RESULTS

Latent Class Clustering (LCC) approach was used in the present study as a preliminary tool for the segmentation of 9,978 crashes. Later, logistic regression analysis was used to identify the factors that influence bicycle crash severity for the whole dataset as well as for the clusters that were obtained from the LCC model. Results of this study show that combined use of both techniques reveals further information that wouldn't be obtained without prior segmentation of the data. Few variables such as season, weather conditions, and light conditions were significant for certain clusters that were hidden in the whole dataset. This study can help domain experts or traffic safety researchers to segment traffic crashes and develop targeted countermeasures to mitigate injury severity.

Health economic assessment of a scenario to promote bicycling as active transport in Stockholm, Sweden

OBJECTIVES

To conduct a health economic evaluation of a proposed investment in urban bicycle infrastructure in Stockholm County, Sweden.

DESIGN

A cost-effectiveness analysis is undertaken from a healthcare perspective. Investment costs over a 50-year life cycle are offset by averted healthcare costs and compared with estimated long-term impacts on morbidity, quantified in disability-adjusted life years (DALYs). The results are re-calculated under different assumptions to model the effects of uncertainty.

SETTING

The Municipality of Stockholm (population 2.27 million) committed funds for bicycle path infrastructure with the aim of achieving a 15% increase in the number of bicycle commuters by 2030. This work is based on a previously constructed scenario, in which individual registry data on home and work address and a transport model allocation to different modes of transport identified 111 487 individuals with the physical capacity to bicycle to work within 30 min but that currently drive a car to work.

RESULTS

Morbidity impacts and healthcare costs attributed to increased physical activity, change in air pollution exposure and accident risk are quantified under the scenario. The largest reduction in healthcare costs is attributed to increased physical activity and the second largest to reduced air pollution exposure among the population of Greater Stockholm. The expected net benefit from the investment is 8.7% of the 2017 Stockholm County healthcare budget, and 3.7% after discounting. The economic evaluation estimates that the intervention is cost-effective and each DALY averted gives a surplus of €9933. The results remained robust under varied assumptions pertaining to reduced numbers of additional bicycle commuters.

CONCLUSION

Investing in urban infrastructure to increase bicycling as active transport is cost-effective from a healthcare sector perspective.

Bicycle crashes and sickness absence - a population-based Swedish register study of all individuals of working ages

BACKGROUND

In recent years, bicycle injuries have increased, yet little is known about the impact of such injures on sickness absence (SA) and disability pension (DP). The aim was to explore SA and DP among individuals of working ages injured in a bicycle crash.

METHOD

A nationwide register-based study, including all individuals aged 16-64 years and living in Sweden, who in 2010 had in- or specialized out-patient healthcare (including emergency units) after a bicycle crash. Information on age, sex, sociodemographics, SA, DP, crash type, injury type, and injured body region was used. We analyzed individuals with no SA or DP, with ongoing SA or full-time DP already at the time of the crash, and with new SA > 14 days in connection to the crash. Crude and adjusted odds ratios (OR) with 95% confidence intervals for new SA were estimated by logistic regression.

RESULTS

In total, 7643 individuals had healthcare due to a new bicycle crash (of which 85% were single-bicycle crashes). Among all, 10% were already on SA or full-time DP at the time of the crash, while 18% had a new SA spell. The most common types of injuries were external injuries (38%) and fractures (37%). The body region most frequently injured was the upper extremities (43%). Women had higher OR (1.40; 1.23-1.58) for new SA than men, as did older individuals compared with younger (OR 2.50; 2.02-3.09, for ages: 55-64 vs. 25-34). The injury types with the highest ORs for new SA, compared with the reference group external injuries was fractures (8.04; 6.62-9.77) and internal injuries (7.34; 3.67-14.66). Individuals with traumatic brain injury and injuries to the vertebral column and spinal cord had higher ORs for SA compared with other head, face, and neck injuries (2.72; 1.19-6.22 and 3.53; 2.24-5.55, respectively).

CONCLUSIONS

In this explorative nationwide study of new bicycle crashes among individuals of working ages, 18% had a new SA spell in connection to the crash while 10% were already on SA or DP. The ORs for new SA were higher among women, older individuals, and among individuals with a fracture.

Analysis of bicycle crashes in Sweden involving injuries with high risk of health loss

Objective: The objectives of the present article were to (a) describe the main characteristics of bicycle crashes with regard to the road environment, crash opponent, cyclist, and crash dynamics; (b) compare individuals who describe their health after the crash as declined with those who describe their health as not affected; and (c) compare the number of injured cyclists who describe their health as declined after the crash with the predicted number of permanent medical impairments within the same population. Methods: A sample of individuals with specific injury diagnoses was drawn from the Swedish Traffic Accident Data Acquisition (STRADA) database (n = 2,678). A survey form was used to collect additional information about the crash and the health-related outcomes. The predicted number of impaired individuals was calculated by accumulating the risk for all individuals to sustain at least a 1% permanent medical impairment, based on the injured body region and injury severity. Results: Nine hundred forty-seven individuals (36%) responded, of whom 44% reported declined health after the crash. The majority (68%) were injured in single bicycle crashes, 17% in collisions with motor vehicles, and 11% in collisions with another cyclist or pedestrian. Most single bicycle crashes related to loss of control (46%), mainly due to skidding on winter surface conditions (14%), followed by loss of control during braking (6%). There was no significant difference in crash distribution comparing all crashes with crashes among people with declined health. The predicted number of impaired individuals (n = 427) corresponded well with the number of individuals self-reporting declined health (n = 421). Conclusions: The types of crashes leading to health loss do not substantially differ from those that do not result in health loss. Two thirds of injuries leading to health loss occur in single bicycle crashes. In addition to separating cyclists from motorized traffic, other preventive strategies are needed.