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January 2020
Shifts In Micromobility-Related Trauma In The Age Of Vehicle Sharing: The Epidemiology Of Head Injury
Joshua Richard Feler
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Recommended Citation Feler, Joshua Richard, "Shifts In Micromobility-Related Trauma In The Age Of Vehicle Sharing: The Epidemiology Of Head Injury" (2020). Yale Medicine Thesis Digital Library. 3898. https://elischolar.library.yale.edu/ymtdl/3898
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Shifts in micromobility-related trauma in the age of vehicle sharing: the epidemiology of head injury
A Thesis Submitted to the Yale University School of Medicine in Partial Fulfillment of the Requirements for the Degree of Doctor of Medicine Joshua R. Feler | Yale School of Medicine | Class of 2020 Advised by Jason Gerrard M.D. Ph.D. | Department of Neurosurgery
1 Abstract ...... 3
2 Introduction ...... 5
3 National trends in rates of micromobility trauma ...... 8
4 Identifying epidemiological differences that may emerge from SMP characteristics ...... 23
5 Behavioral differences potentiating high risk mechanisms ...... 41
6 Conclusion ...... 55
7 Bibliography ...... 57
8 Appendices ...... 67
Feler | 2
1 Abstract Shared micromobility programs (SMPs) provide access to a distributed set of shared vehicles – mostly conventional bicycles, electronic bicycles, and electronic scooters – and are increasingly common in domestic and global cities, with riders completing an estimated 84 million trips using an SMP vehicle. There is heterogeneity in these programs in size, vehicle types offered, and distribution model. The impact of SMP introduction on the epidemiology of traumatic injury is largely unknown, and the relative safety of different shared vehicle types has not been evaluated; these effects are the subject of this study.
Considered as a whole, the annual number of traffic-related bicycle deaths in the United States has been increasing in the last decade. The 30 most populous cities in 2010 were selected for closer analysis. For each year in each city from 2010 to 2018, the crude rate of traffic-related bicycle deaths per-person and per-trip was calculated, and the year in which any SMP was introduced was identified. Interrupted time-series analysis demonstrated that SMP introduction was not associated with changes to these rates but was associated with an increase in estimated number of bicycle trips.
National data suggest that rider demographics, and therefore population at risk, may shift with the availability of new vehicle types and SMPs. Injured e-scooter riders, in particular, have near parity in the gender of injured riders, a stark contrast to the nearly 3 to 1 ratio of males in bicycle trauma, and SMP riders are disproportionately young adults. The importance of these shifts was highlighted in analysis of the 2017 National Trauma Database®, which yielded 18,604 adult patients. This analysis showed that older age, male gender, accident involving a motor vehicle, and failing to use a helmet were associated with more severe injuries and mortality. It also demonstrated that the risk reduction afforded by helmets to females was less than the same for males in multivariate analysis. These findings contextualize a review of studies of trauma involving motorized micromobility vehicles.
Finally, to explore mechanisms of differential injury by vehicle type, structured observations of riders of personal and shared vehicles were performed in San Francisco over 2 months in the spring of 2019. In total, 4,472 riders were observed, approximately a fifth of whom
Feler | 3 used a shared vehicle. Riders of shared vehicles were more likely to use a motorized vehicle including e-scooters and e-bicycles, but helmet use was lower among this cohort (37.3%), compared with riders of personal vehicles (84.6%). Use of a shared vehicle, an e-scooter, and a dockless shared vehicle were associated with decreased likelihood of helmet use. Nonetheless, shared vehicle riders were equally likely to observe traffic regulations. Riders of e-scooters were more likely to stop correctly at intersections but also more likely to ride on the sidewalk than riders of conventional bicycles (c-bicycles) and electronic bicycles (e- bicycles).
Given the popularity of SMPs and their success in augmenting urban public transport systems, some form of SMP will likely remain a fixture in urban environments for the foreseeable future. The data collected here provide motivation for and guidance in developing safer SMPs and can potentially be used as agents of public health to tailor SMP characteristics to support safe practices and protect vulnerable road users.
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2 Introduction
The Evolution of Shared Micromobility
Personal transportation is undergoing a revolution. Where before choices were generally limited to automobiles, public transit, motorcycles, mopeds, bicycles, or walking, new technologies have brought an array of products facilitating movement through cities. The miniaturization of electric motors and batteries—not to mention reliable disc-style brakes—has made possible the manufacture of electronic vehicles that enable riders to travel further, over more challenging terrain, and with heavier loads without corresponding increase in physical effort. Widespread adoption of smartphones and GPS-enabled devices has facilitated the commercialization of shared vehicles deployed through SMPs that offer rental bicycles and scooters. Distributed throughout urban environments, these have been touted as solutions to the ‘first-mile last-mile problem,’ filling large gaps between stations in a public transit network.1 Additionally, the surveillance economy2 has funded the rapid deployment of large fleets of cheaply available shared conventional bicycles (c-bicycles), electronic bicycles (e-bicycles), and electronic scooters (e-scooters) domestically and globally.
In 2012, the first public SMP in the United States of America was installed in Washington, D.C., and it offered 120 c-bicycles distributed among 10 stations.3 By the end of 2018, there were over 57,000 shared c- and e-bicycles in cities across the US, on which riders completed 36.5 million trips over the year.4 E-scooter rental programs grew even more rapidly. The first shared e-scooter program was implemented in Santa Monica, CA in September of 2017, and by the end of 2018, 85,000 e-scooters were deployed in urban environments across the nation.5 Despite their newness, 38.4 million of the total 84 million trips by SMP riders in 2018 were on an e-scooter.4
SMPs differ in scale, distribution model, and vehicle type. Some cities have fewer than 100 vehicles, while others have thousands. At peak in Austin, TX, there were as 17,650 e- scooters from several companies deployed,6 about 1 per 44 citizens. There are two main distribution models: “station-based” SMPs require that vehicles be rented from docks Feler | 5 distributed throughout a region, and “dock-less” SMPs allow their riders to start and end journeys at any point within a geographically defined area. Common vehicle types include c-bicycles, e-bicycles, and e-scooters, although low-speed sit-on scooter models are also available in certain cities to provide greater accessibility for riders with physical disabilities.7,8 Selected characteristics of representative vehicles deployed by SMPs are given in Table 2.1.
Figure 2.1: Shared C-bicycles, E-bicycles, and E-scooters
Table 2.1: Characteristics of Typical Shared Vehicles Category Provider Governed Speeda Weight Motor Power Stand-on e-scooter Bird 15 mph 26.9 lbs 250 W Sit/stand e-scooter Ojo 20 mph 65 lbs 500W Jump 20 mph 78 lbs 250 W E-bicycle Ford 18 mph 68 lbs 350 W E-mopedb Scoot 30 mph 232 lbs 1400 W a Governed speed indicates the maximum speed at which the motor will continue to accelerate the vehicle. Vehicles may travel at speeds greater than the governed speed (e.g. riding downhill), but the motor will not contribute to maintaining this speed. b E-mopeds are not generally not grouped within shared micromobility but are provided here for context.
Important differences may arise not just from the capacities of the vehicles but also from dependent shifts in the behaviors and demographics of riders. For example, one-way trips and mixed-mode trips in which the use of a shared vehicle might comprise only a single leg of a journey are possible. Although many examples of this trip pattern would be benign (e.g. deciding to use a bicycle to return home from work on a sunny afternoon), others are not (e.g. deciding the same while intoxicated). Similarly, motorized vehicles might attract riders that are either less physically capable, e.g. the elderly, or less experienced. As will be
Feler | 6 shown, these SMPs are accompanied by an interdependent mixture of shifting demographics and on-road behavior that may shift the epidemiology of traumatic bicycle injury.
The emergence of new vehicle types and ownership models has heralded much discussion of their impact on the urban environment including effects on public safety, challenges in regulating services, and data-reporting practices of companies; still there remains little published data describing the epidemiological effects of these programs on traumatic injury. Rates of head injury are of particular interest as they are a common cause of morbidity and mortality among riders of bicycles and scooters, and helmets provide a protective effect to such injuries.9 Head injury may be the cause of death in as many as 75% of fatal bicycle accidents.10 Given their popularity and theoretical benefits to urban transport systems—specifically decongesting roads by shifting occupants out of automobiles11—SMPs will likely continue to spread through urban environments. It is important that safe practices be identified to guide the expansions and innovations that shape the future of these programs.
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3 National trends in rates of micromobility trauma
Before discussing the relative safety of the different varieties of SMP, it must be assessed whether they can be implemented safely in any form. As will be shown, the first SMP introduced in most cities is a bikeshare, and c-bicycles remain the dominant form of micromobility in general. For that reason, this section assesses for changes to c-bicycle- related mortality with the introduction of the first SMPs in large cities to explore their impact on mortality.
Bicycle-related trauma in the United States
Nationally, rates of bicycle injury are rising. From 1998 to 2013, there was a 28% increase in the number of injuries and a 120% increase in the number of hospitalizations attributed to bicycle accidents. The odds of head injury increased by 10% over the same period.12 In 2018, the Center for Disease Control estimated 160,644 emergency room visits for traffic- related bicycle injuries.13 Mortality has also risen from 727 to 857 deaths between 2004 and 2018.14
Compared to other developed nations, these numbers reflect considerably greater danger to domestic cyclists. In 2010 in the United States, there were an estimated 10.3 deaths per million miles traveled, much greater than the 2.9 in Germany, 2.2 in the Netherlands, and 2.4 in Denmark (converted from reported per-kilometer rate).15 As can be seen in Figure 3.1, the increase is particularly prominent in urban environments while the total number of deaths in rural areas has remained fairly stable.
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Figure 3.1: Traffic related Bicycle Fatalities in the United States
700
600
500
400
300
200
100
0 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
Rural Urban Linear (Rural) Linear (Urban)
N.B.: Diamonds indicate the year of introduction of bikeshare in the United States. Data from NHTSA Fatal Accident Reporting System14
The impact of bikeshare on traumatic bicycle injury
The impact of SMPs on population-level measures of safety is largely unknown. A study of trauma registries from North American cities before and after the introduction of SMPs showed that the overall rate of trauma-team activations for bicycle accidents in cities fell by 28% after SMP introduction compared to an increase of 2% in control cities without a SMP over the same period. The SMP cities were Montreal, Washington DC, Minneapolis, Boston, and Miami Beach; control cities were Vancouver, New York, Milwaukee, Seattle, and Los Angeles. However, the odds of head injury increased by 30% in SMP cities but decreased by 6% in control cities, which the authors attributed to low utilization of helmets among SMP riders. The authors were conservative in their interpretations: the introduction of SMP into a city increases the odds of head injury for injured bicyclists (aOR 1.3). Citing a lack of data describing rates of bicycle riding and rates of injury not causing a trauma activation, the authors do not interpret their findings to mean that SMPs decrease the overall incidence of traumatic bicycle accidents.16 Moreover, this study predates the introduction of dockless vehicles and motorized vehicle to SMP fleets, limiting its generalizability to the present circumstance. Feler | 9
Editorial commentary on this article interpreted the data to suggest that, regardless of the increased prevalence of head injury among injured cyclists, the presence of bikeshare had a protective effect? as the overall reduction in number of accidents lead to a 6% absolute reduction in incidence of bike-related rate head injury,17 though the authors refuted this conclusion in response.
Since the publication of this solitary study, SMPs have expanded/been introduced to numerous urban environments, allowing examination of the phenomenon via national traffic safety resources such as the Fatal Accident Reporting System. This federally maintained database includes traffic accidents on public rights-of-way that lead to death within 30 days of the event, contains data extending to 2004, and includes ‘pedalcyclists.’14
Methods
Sample Identification and Data Collection Based on 2010 city population estimates18 from the United States Census as reported by the United States Census Bureau, the 30 most populous cities in the United States were identified. Total population estimates were collected from intercensal United States census estimates.18,19 Total numbers of fatal bicycle accidents per-city annually from 2004 to 2018 were collected from the Fatal Accident Reporting System provided by the National Highway Transport Safety Administration.14 Population rates of bicycle commuting were collected form the American Community Survey,20 and missing values were imputed using a Kalman filter.21 The year of the first introduction of a SMP anywhere within each city was identified by review of news reports and publicly available documents from local governing bodies.
Data Analysis
Estimating Number of Bicycle Trips Because of the limitations of available data, rates of bicycle commuting are used as a proxy for overall rates of bicycle riding. The annual number of bicycle trips is estimated from the
Feler | 10 rate of bicycle commuting in each city’s population by the formula proposed by Barnes and Krizek22:
3% + % bicycle commuting × population × 365 100
Defining Rates of Bicycle Fatality Two crude rates of bicycle fatality will be calculated and analyzed:
1) Crude rate per trip (CRPT): a ratio of deaths to number of bicycle trips. Expressed in units of deaths per 100,000,000 trips. 2) Crude rate per person-year (CRPP): a ratio of deaths to person-years. Expressed in units of deaths per 100,000 person-years.
Overall Trends in Bicycle Use and Fatality Data from all cities were summed by year, yielding sample-wide values for population, bicycle trips, deaths, CRPP, and CRPT. Univariate linear regression was performed assessing for change of these values through time.
Univariate Pre-Post Comparison To examine the short-term impact of the implementation of SMP and reduce the effects of long-term trends on changes in fatality rates, the 2 years prior to implementation were compared to the 2 years following implementation, excluding the year of implementation, in order to limit the effects of long-term changes. Aggregated number of bicycle trips, CRPP, and CRPT were calculated for pre- and post-implementation periods. The percent difference between pre- and post-implementation values were calculated. A crude rate of fatal bicycle accidents was calculated for the entire population pre- and post- implementation. Pre- and post-introduction crude death rates were tested for difference with a paired t-test. Significance was defined as p<0.05 for a two tailed test of significance.
Interrupted Time Series Regression Univariate linear regressions of the presence of SMP as a predictor of CRPP, CRPT, and bicycle trip count were calculated for each city for all available years of data (without Feler | 11 excluding cities with fewer than 3 years of data), and the resulting regression values were exponentiated to yield rate ratios. A pooled rate ratio and confidence interval were calculated from these values.
An interrupted time series model23,24 was calculated that included the presence of bike share, the duration of the implementation, the density of bicycle infrastructure (miles per mi2). This model was based on the following function:
log(rate) = β0 + β1×Ty + β2× I + β3×Ti
where β are experimentally determined constants, Ty is the year, I is a binary variable representing the presence or absence of SMP, and Ti is the number of prior complete years of experience. The model was fit with a Quasi-poisson distribution to account for over- dispersion. The exponentiated form of β2 is reported with 95% confidence intervals for each city. This value corresponds to the effect of SMP on the first complete year of its presence. Although other time points are not reported, their presence in the model contributes to the estimation of the rate ratios. Also, the differences in the rate of change post-implementation (β3) may also be attributable to bikeshare, it does not contribute to the first year as Ti is 0 at this point. A pooled value and variance for β2 were calculated to determine overall effect. Those cities that did not have 2 post-implementation years were excluded from this analysis. Significance was defined as p<0.05 for a two-sided t-test.
Results
Aggregate Trends in Bicycle Use and Fatality The total number of bicycle deaths in the sample was 1,410 which yields an overall CRPP of 0.243 deaths per 100,000 person-years and a CRPT of 15.0 deaths per 100 million bicycle trips. Results from linear regressions of the entire sample are given in Table 3.1.
The number of fatalities per year increased through time at a rate of nearly 2 per year (p=.007). CRPP trended towards an increase (p=.09), but CRPT did not change significantly through time. Both the population and number of bicycle trips taken increased
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(p<.001). The number of trips per person also increased by 0.27 per year (p<.001). CRPP and CRPT are plotted through the study period in
Figure 3.2.
Table 3.1: Trends in Bicycle Use and Fatalities from 2004 to 2018 Value Rate of Change P R2 Fatalities 1.95 deaths per year .007 0.44 Population 336,298 persons per year <.001 0.98 Number of Trips 16,049,714 trips per year <.001 0.98 CRPP .00283 deaths per 100,000 person-years per year 0.09 0.21 CRPT -0.094 deaths per 100,000,000 trips per year 0.35 0.07
Figure 3.2: CRPP and CRPT from 2004 to 2018
Univariate Pre-Post Comparison Twenty-six cities (86.7%) had 2 complete post-implementation calendar years of SMP within the study period, and the first SMP in all cities offered c-bicycles. The total number of deaths was 177 in the pre-implementation period and 203 in the post-implementation
Feler | 13 period. These values yield a CRPP of 0.23 per 100,000 person-years pre-implementation and 0.26 deaths per 100,000 person-years post-implementation and CRPT of 14.1 and 15.3 deaths per 100 million bicycle trips respectively.
In per-city analysis summarized in Table 3.2, all cities but 4 had an increase in the volume of bicycle trips in the post-implementation periods with a non-significant increase of 847,553 trips per year (95%CI: -118,473 – 1,813,581, p = 0.08). Thirteen demonstrated a decrease in the CRPP in the post-implementation years, and 13 demonstrated an increase. Thirteen showed a decrease in CRPT, and 13 showed an increase in CRPT. In paired t- testing, CRPP increased by 0.076 deaths per 100,000 person-years (95%CI: 0.009 – 0.143, p = 0.03), and CRPT non-significantly increased by 4.18 deaths per 100 million bicycle trips (95%CI: -0.60 – 8.97, p =0.08).
Table 3.2: Change in Rates of Fatal Bicycle Accident by City CRPP (deaths per 100,000 CRPT (deaths per 100 Million Trips (n) person years) 100,000,000 trips) City Pre Post % Changea Pre Post % Changea Pre Post % Changeb New York city, New York 0.24 0.20 -16.8 15.6 11.9 -23.7 1.25 1.39 10.9 Los Angeles city, California 0.31 0.46 51.9 17.7 27.0 52.7 0.68 0.68 1.0 Chicago city, Illinois 0.28 0.24 -13.8 15.5 12.4 -19.9 0.48 0.52 8.2 Houston city, Texas 0.19 0.24 30.1 14.2 17.8 24.9 0.28 0.31 10.1 Philadelphia city, 0.10 0.16 64.9 4.4 7.1 60.9 0.34 0.35 3.6 Pennsylvania Phoenix city, Arizona 0.53 0.50 -5.4 35.8 33.8 -5.4 0.22 0.24 5.7 San Antonio city, Texas 0.08 0.32 326.0 6.6 26.8 307.0 0.15 0.17 10.7 San Diego city, California 0.18 0.04 -80.6 11.3 2.2 -80.9 0.22 0.23 4.9 Dallas city, Texas 0.16 0.04 -76.2 13.6 3.2 -76.7 0.15 0.16 7.4 San Jose city, California 0.26 0.34 34.0 16.1 21.2 31.7 0.16 0.17 6.3 Jacksonville city, Florida 0.57 1.00 73.9 41.0 71.6 74.5 0.12 0.06 -48.4 Indianapolis city (balance), 0.18 0.41 129.0 13.3 29.9 125.0 0.11 0.12 3.9 Indiana San Francisco city, California 0.12 0.35 188.0 4.2 10.8 156.0 0.24 0.28 17.2 Austin city, Texas 0.24 0.11 -53.8 12.8 5.8 -54.5 0.16 0.17 9.9 Columbus city, Ohio 0.31 0.41 33.1 21.0 26.9 28.4 0.12 0.13 9.1 Fort Worth city, Texas 0.13 0.12 -6.4 11.3 10.1 -10.7 0.09 0.10 11.9 Charlotte city, North 0.13 0.19 40.0 11.4 15.2 33.8 0.09 0.10 12.1 Carolina Detroit city, Michigan 0.37 0.30 -19.4 25.9 18.5 -28.5 0.10 0.05 -44.0 El Paso city, Texas 0.07 0.07 -0.8 6.4 6.1 -5.4 0.08 0.08 5.7 Baltimore city, Maryland 0.16 0.08 -48.6 10.5 5.4 -48.9 0.10 0.09 -2.2 Boston city, Massachusetts 0.24 0.46 90.4 13.3 22.5 70.1 0.11 0.13 17.6 Feler | 14
Seattle city, Washington 0.23 0.21 -7.7 7.8 6.8 -13.3 0.19 0.22 15.3 Washington city, District of 0.09 0.17 91.3 4.1 7.5 81.7 0.12 0.13 10.1 Columbia Nashville-Davidson metropolitan government 0.08 0.00 -100.0 6.6 0.0 -100.0 0.08 0.08 2.9 (balance), Tennessee Denver city, Colorado 0.17 0.24 39.1 8.7 10.4 19.2 0.12 0.14 25.8 Louisville/Jefferson County metro government (balance), 0.32 0.97 198.0 24.7 72.8 195.0 0.08 0.04 -49.1 Kentucky Milwaukee city, Wisconsin 0.00 0.25 - 0.0 15.3 - 0.09 0.10 4.4 Portland city, Oregon 0.16 0.31 92.4 3.5 6.4 83.5 0.29 0.31 9.0 Las Vegas city, Nevada 0.33 0.23 -28.2 24.3 17.6 -27.6 0.08 0.09 3.5 a Red color marks an increase in CRPP or CRPT and thereby an endangering effect, and green color marks a decrease in CRPP or CRPT and shows a protective effect. b Blue color marks an increase in trip counts, and yellow color marks a decrease in trip counts.
Interrupted Time Series Regression Per-city results for the rate ratios calculated from univariate and multivariate models are given in Table 3.3, and pooled odds ratios are summarized in Table 3.4. Baltimore was excluded from multivariate modeling of CRPT and CRPP as models did not converge, likely because of numerous years with 0 deaths both before and after implementation. Bicycle trip volume was significantly increased after SMP introduction in all but 1 city in univariate analysis but only 4 after accounting for the effects of time trends. The pooled rate ratio for bicycle trip volume was significant, indicating a 3% increase in the number of bicycle trips per year.
In four cities, the rate ratio for CRPP was significantly different after the implementation of SMP, with 3 increased and 1 decreased. After the inclusion of time trends, all 3 cities that demonstrated significant increases in CRPP in univariate analysis become nonsignificant. Dallas, the 1 that showed a significant decrease in CRPP in univariate analysis, remained significant, and San Antonio newly demonstrated a significant increase in CRPP. The pooled rate ratio for CRPP was not significant in univariate or multivariate analysis. CRPT followed the same pattern as CRPP.
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Table 3.3: Effects of Bikesharing on CRPP, CRPT, and Trip Volume
Univariate Multivariate
Trip Volume Trip
CRPP
CRPT
Table 3.4: Pooled Rate Ratios from per-city Analysis Attribute Univariate Rate Ratio Multivariate Rate Ratio Model CRPP 1.14 [0.97 to 1.33] 1.11 [0.86 to 1.43] Quasi-poisson CRPT 1.05 [0.89 to 1.24] 1.10 [0.85 to 1.42] Quasi-poisson Trip Volume 1.20 [1.16 to 1.23] 1.03 [1.02 to 1.05] Quasi-poisson Feler | 16
Discussion
This analysis reveals that, although there has been a rise in the absolute number of bicycle fatalities in the United States, this change may be mediated by an increase in the total number of trips completed rather than an increase in the risk associated with each individual ride. Though there was a rise in the crude rate of death from traffic-related bicycle accidents, there was an overall rise in trip volume, and CRPT, an approximation for the individual risk of death associated with each bicycle trip, remained stable.
SMPs were common in the large urban environments examined, having been introduced in all but 4 of the study cities prior in 2016 or earlier, and their introduction was only associated with increasing the volume of bicycle trips completed. Though there was a significant difference between pre- and post-implementation CRPP in univariate testing, this effect was not sustained in the interrupted time-series regression. Given that the latter accounts for baseline trends, it is likely that the initially observed difference resulted from confounding with exogenous effects on the rates of fatal bicycle accident such as the increased rates of cycling in urban environments documented here.
Estimates for overall CRPP and CRPT are consistent with prior reports. The overall CRPP was equal to the 0.24 deaths per 100,000 person-years reported by Teschke et al., who used the same method but in an earlier time period.25 The overall CRPT of 15 deaths per 100 million bicycle trips lies between the 8 reported by Beuhler and Pucher15 and the 21 reported by Beck et al.26 Each of these uses a slightly different study period and geographical sample, so averaging effects with areas or times of greater death to bicycle trip ratio are possible. The trip volume estimate is also different in each (though formulae are not given in the other studies), which may contribute to the observed differences.
That there would be no increase in the crude rate of fatal accidents after the introduction of SMP is not necessarily surprising: a review of news reports reveals only 4 riders of shared bicycles have been involved in fatal accidents, all occurring in cities included in this study. Excluding one of these deaths which occurred after the study period, this comprises 0.6% Feler | 17 percent of the 363 deaths that occurred after SMP implementation in this sample. Moreover, a study by Martin et al. which assessed SMP-associated collisions rates estimated that there were 442 injuries per 100 million bicycle trips27 in the Washington D.C.-based Capital Bike Share compared with the estimated 1,46126 and 1,39825 reported in other studies for the general population, possibly indicating that bikeshare riding has decreased risk per-trip. Of the 4 SMP bicycle deaths that have occurred at the time of writing, worst-case analysis (per-trip rate calculated from total number of trips completed before the fatality and ignoring trips completed after), yields the following results for CRPT which do not provide a clear pattern for the relationship between CRPT for SMPs and general bicycle riding.
Table 5: Estimated Bikeshare and Community CRPT Worst Case SMP Estimated CRPT 2 years City Date Trips Prior CRPT Post SMP Chicago 7/2016 6,397,65828 15.6 12.4 NYC 6/2017 38,103,12029 2.6 11.9 NYC 4/2019 72,098,74629 2.8 11.9 SF 3/2019 3,217,04730 31.1 10.8
The increase in bicycle trips in this sample is consistent with national trends: based on the American Community Survey, the estimated number of bicycle commuters increased from about 488,000 in 2000 to about 786,000 in 2008–2012, approximately 0.5% of the total population of the United States.31 The proportion of trip volume increase that can be attributed to rides on an SMP vehicle cannot be assessed due to the paucity of published data on SMP utilization, but it is likely that not all new riders used SMP vehicles. For example, approximately 2 million trips were completed on shared bicycles in San Francisco in 2018,30 about 6% of our the 30 million total trips that were estimated for that year. These trips comprise about one half of the growth in annual trips between the year prior to SMP introduction and 2018. In contrast, 17.6 million trips were completed on shared bicycles in New York City in 2018,29 which was 12% of the estimated trips in that year but nearly 100% of the growth between the year prior to SMP introduction and 2018. SMP trip
Feler | 18 counts were not available for every city, but it is clear that the effect on growth is heterogenous and likely includes both a bulk increase due to SMP use but perhaps also ecological effects in which they contribute to changes in personal bicycle use as well.
The trend towards decrease in CRPT with the rise in trip volume may exemplify a ‘safety- in-numbers’ effect whereby the volume of bicycle and pedestrian traffic can be increased at a greater proportion than injuries associated with those trips. This effect is demonstrated in meta-analysis of epidemiological models32,33 as well as individual case studies,34 but large numbers of drivers choosing to walk or ride instead may be required to achieve this effect. Simply encouraging more bicycle and pedestrian traffic without decreasing car traffic may be inadequate.35 There is evidence to suggest that SMPs may contribute to both increasing the absolute number of riders and decreasing the number of automobiles. A study including Melbourne, Brisbane, Washington D.C., Minnesota, and London suggested that the mode-shift effect is mixed and possibly proportional to the percentage of people that commute by car. In the cities with greater than 70% car commuting rates, around 20% of bikeshare users reported that shared bicycle use had replaced car use. In comparison, in the two cities with car commuter rates of around 40%, only 2% and 7% of bikeshare users reported replacing a car trip.11 This study also showed that the greatest proportion of riders responded that they had replaced a walking or public transit trip with the bike trip, and others have shown decreased use of certain kinds of public transit after SMP introduction36,37 with uncertain effects on the density of pedestrian traffic – an important features of the ‘safety-in-numbers’ model.
There are important limitations to the statistical approach used in this analysis. First, the introduction of an SMP is often part of a larger effort to improve the rideability of a city,38 so no effect described can be conclusively attributed to the presence of SMPs but rather the global efforts of a city that increase micromobility traffic and the safety of vulnerable road users. These efforts are perhaps best demonstrated by Vision Zero programs that exist in several cities in the United States, a network of multidisciplinary teams aiming to bring total traffic fatalities to zero through a data-driven systems approach.39
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The impact of concurrent investment in bicycle infrastructure in particular has uncertain but likely beneficial effects on accident rates and severity. Although a 2015 Cochrane review identified a lack of high-quality evidence for the efficacy of bicycle infrastructure in reducing the rate of collisions and increasing cycling utilization due to heterogeneity in outcome reporting and study design,40 there are statistical and anecdotal reports describing both effects. For example, in Boston, the total mileage of bicycling infrastructure increased from 0.034 miles to 92.2 miles over 7 years alongside improvements to signage, parking, cyclist awareness campaigns, and the addition of a c-bicycle SMP. Over that time, the percentage of the population commuting by bicycle increased from 0.9% to 2.4%; the percentage of bicycle accidents causing injury decreased from 82.7% to 74.6%, though the absolute number of accidents increased.41 This study suggests that multi-modal approaches to road safety that include infrastructural improvements may attract and protect riders, but, as the authors of the Cochrane review note, further research is required in this domain to guide decision making.40 Limited data exist regarding the density of bicycle infrastructure in the study cities, however, the sampling frequency was too low for inclusion in this analysis as data were only available for 2007, 2009, 2014, and 2018.42–46 In the case that investment in bicycle infrastructure increases trip volume and decreases mortality rate and that these improvements are concurrent with SMP introduction, ascribing the effects identified in this interrupted time series regression to SMP introduction would be a less defensible position.
A second limitation is that there is heterogeneity in number of vehicles deployed among the sample with uncertain implications for the findings of this analysis. In order to homogenize the sample in this study, years with very small numbers of shared vehicles (less than 100) were considered as pre-implementation in this study. Although the volume of overall bicycle trips was estimated, actual rates were unavailable, and the estimate was not validated in the presence of SMP. There is evidence that the introduction of SMP might change the ratios of commuter trips to trips of other kinds and thereby decrease the accuracy of the estimate. Surveyed riders of shared bicycles in Washington D.C. had significantly different distributions of trip purpose with a greater percentage of the sampled
Feler | 20 long-term subscribes to the bike making utilitarian trips and many of the non-subscribing users making trips for tourism.47 The overall impact of these differences on bicycle trip volume is unknown.
Third, it unclear based on available documentation how novel vehicle types such as e- bicycles and e-scooters are represented in the FARS dataset (if at all), and the years of available data do not allow robust examination of the introduction of dockless bicycles and e-scooters into cities. Three study cities introduced dockless e-bicycles in 2017, 11 introduced them in 2018, and 1 introduced them in 2019 (total 15 of 30). E-scooters were adopted much more rapidly, introduced in 17 cities in 2018 and 4 cities in 2019 (total 21 of 30). In contrast to the marginal impact of bicycle SMP on rates of fatal bicycle accidents reported here, shared e-scooters may prove to be a comparatively dangerous form of transportation. Since first appearing in 2017, there are 18 documented deaths of riders of shared e-scooters compared to 4 associated with bicycle SMP since its introduction in 2008. It remains to be seen whether this reflects an intrinsic lack of safety in the vehicle type or whether the rate of their deployment simply overwhelmed local infrastructure. On account of these deaths, several cities have temporarily banned e-scooters from the streets and are re-permitting with use restrictions such as disallowing night-time riding as in El Paso, TX.48
Most micromobility accidents are not fatal, and this analysis is therefore limited in describing the overall effect of SMP introduction on safety. A review of bicycle accidents in the national trauma registry who presented with intracranial bleeds yielded a mortality rate of 2.8%, likely an over-estimation of the general rate give the inclusion criteria of having an intracranial bleed and the fact that patients that presented to the emergency department with lesser injuries would not be accounted in this database.49 Individual-level data is require for more nuanced assessments of the impact of SMP introduction on associated trauma. With that said, it is reasonable to conclude from this analysis that the addition of bicycle SMPs to urban environments does not cause drastic increases in mortality, and it may contribute to increases in rates of bicycling.
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Section Summary
• National rates of bicycle fatality are rising, but so is the volume of bicycle traffic. Overall, the risk of death per trip appears to be stable. • Bikeshare services were implemented in many urban areas, and they have increased the number of bicycle trips taken without increasing crude rates of death per person-year or per trip.
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4 Identifying epidemiological differences that may emerge from SMP characteristics
Where the prior section established that SMPs can be introduced without dramatic increases in traffic-related mortality, it also suggested that non-bicycle SMPs (i.e. e- scooters) may create a differential risk. SMPs may attract riders of a different epidemiological profile compared to riders of personal vehicles which may have important implications for the nature of associated atraumatic injury. In the absence of data that allow direct examination of these effects, this section explores available data to obtain indirect evidence.
Increasing age in bicycle trauma
National data suggest that death due to traumatic bicycle injury is increasingly common among older adults and the elderly. The proportions injured and hospitalized bicyclists over the age of 45 increased by 81% and 66% respectively between 1998 and 2013, driving the overall increase in the number of injuries and hospitalizations observed during that period.12 This trend is also reflected in the increasing average age of a rider involved in a fatal bicycle accident documented in the FARS database as shown in Figure 4.1.
Figure 4.1: Mean Age of Rider in Fatal Bicycle Accident 2004-2018
60
50
40 R² = 0.9796 30
20
10
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Data from NHTSA Fatal Accident Reporting System14
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Shifts in non-fatally injured rider demographics
Although the FARS database only provides information on fatal accidents, the National Electronic Injury Surveillance System (NEISS) is a federally maintained probability- sample of patients presenting to emergency rooms after injury related to numerous products including bicycles, e-bicycles, and e-scooters. In this database, patients are described with basic demographics information, a brief narrative description of the incident, and codes describing associated products and injuries. Fatal accidents are excluded by definition. Each record describes an individual patient, and a calculated weight can be used to estimate a national burden of similar injuries.50 Helmet use was estimated with the accident narrative search method described by Graves et al.51 Entries for patients below the age of 18 were excluded in the given analysis. Due to the survey methodology, contextualization of injury rates by local conditions such as the population and presence of SMP within a city (and thereby the interrupted time-series analysis within a city could not be performed. Nonetheless, these data lend general insight into the demographics of injured riders nationally as well as highlight the importance of assessing non-fatal trauma.
Estimated national rates of injury of adults from this database are plotted in Error! Reference source not found.. As can be seen, rates of non-fatal injury due to c-bicycling have been stable while injuries due to e-bicycles and e-scooters are rising. C-bicycle injury remains far more frequent than either of the other groups, regardless.
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Figure 4.2: NEISS Non-Fatal Injury Estimates.
A: estimated number of injuries from 2009 to 2018 for c-bicycles, e-bicycles, and e-scooters. B: The same data as A without c-bicycles and zoomed in to demonstrate changes in e-bicycles and e-scooter injuries more closely.
Summary statistics for data from 2009 to 2018 are given in Error! Reference source not found.. While the c-bicycle and e-bicycle groups are male dominated, there is relative gender parity amongst injured riders of e-scooters. The mean age was similar between the groups. The rate of hospital admission was highest among riders of e-bicycles and lowest among riders of e-scooters, perhaps signaling a greater severity of injury.
Table 6: Summary Statistics of Injured Micromobiltiy Users from 2009 to 2018 C-bicycle (N=2,708,799) E-bicycle (N=125,425) E-Scooter (N=82,690) Age Mean (SD) 42.030 (16.848) 38.802 (15.590) 42.062 (17.661) Sex Unk 86 (0.0%) 0 (0.0%) 0 (0.0%) Male 1997503 (73.7%) 95351 (76.0%) 44862 (54.3%) Female 711210 (26.3%) 30075 (24.0%) 37829 (45.7%) Helmet Use Yes 177335 (6.5%) 12137 (9.7%) 3172 (3.8%) No 162884 (6.0%) 10310 (8.2%) 2896 (3.5%) Unknown 102978 (82.1%) 76622 (92.7%) 2368580 (87.4%) Head Injury 424989 (15.7%) 18472 (14.7%) 11211 (13.6%) Concussion 68749 (2.5%) 2986 (2.4%) 2323 (2.8%) Internal Head Injury 294235 (10.9%) 13763 (11.0%) 6768 (8.2%) Hospitalized 304532 (11.2%) 20195 (16.1%) 7626 (9.2%)
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Age distribution by vehicle type is given in the density plot shown in Figure 4.3. This plot is notable for the relative prominence of the 25 to 40 age group and involvement of patients over the age of 65 among e-scooter riders. In total, 12.1% of e-scooter riders were over the age of 65 compared to 10.5% among c-bicyclists and 6.3% among e-bicyclists.
Figure 4.3: Age of Rider by Vehicle Type
Although no further comments can be made regarding the impact of SMPs specifically, the addition of these data to the analysis of fatality rates prior underscores the importance of accounting for novel vehicle types in future assessment of road safety as well as highlighting the dynamic nature of transport safety in recent years.
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Demographic differences in riders of SMPs
An observational study of riders demonstrated that females comprised 24% of riders of shared bicycles compared to 36% of riders of personal bicycles.52 Another showed a possible difference in gender distribution based on trip purpose. Among commuters, females comprised 29% of both shared and personal bicycle riders, but, among casual riders, females comprised 48% of shared bicycle riders and 33% of personal bicycle riders.53 A third study by Buck et al. combining survey data with in situ observations found that 52% of shared bicycle riders were female compared to 35% among community bicyclists.47 The increased representation of females among SMP riders may contribute to increases in the number of traumatically injured female patients by reducing the male skew in the at-risk population.
There is comparably little published data about differences in the age of shared and personal bicycle riders. The above study by Buck et al. also collected information about the age of riders and showed that SMP riders were younger than the average community bicyclist (34 vs. 42 years old). While nearly 3/4 of community bicyclists were over the age of 35, almost half of shared bicycle riders were between the ages of 25 and 35.47 If SMPs are more commonly used by younger people, it is unlikely that they contribute to the increasing age of traffic related bicycle mortality.
It is not currently feasible to identify micromobility vehicle types in registry data, so the importance of these shifts in demographics will be examined by proxy in traumatically injured c-bicycle patients.
Methods
Patients 18 years or older presenting after bicycle accident were identified in the 2017 National Trauma Data Bank® using ICD10 for bicycle injuries as given in Appendix 1. Demographics, helmet use, injury severity scores, and short-term outcomes were extracted, and involvement in a motor vehicle collision,54 injuries, and procedures were identified by ICD10 code (code dictionary in Appendix 1). Analysis was performed in R Studio (Version 1.2.5019, RStudio, Inc.). Records missing data on the patient’s gender were discarded.
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Continuous variables (age and body-mass index) were normalized against the entire sample. After this step, none of the independent covariates were found to have missing values except for blood alcohol level testing, which was assessed with Little’s Test for Missing Completely at Random and found not to be missing completely at random (p<0.001). Values were not imputed, summary statistics are given with the denominator as the total number of records with data for that variable, and the number of missing values was reported where greater than 0.
Records were grouped by gender, and descriptive statistics were performed on extracted data with chi-square testing for categorical variables and t-testing for binary and continuous variables. Differences in proportion represented by a single group within a categorical variable were assessed by t-test. Records were further subdivided by helmet use, and descriptive statistics were recalculated. Separate multivariate logistic regression models of outcomes including age, helmet use, involvement in motor vehicle collision, and anticoagulant as independent variables were calculated for males and females. These groups were then combined, and the models were recalculated with the addition of a gender term and an interaction term for gender and helmet use. This set of independent variables were identified through review of the findings of similar studies in the literature. Model parameters were exponentiated and reported with 95% confidence intervals and p-values. The exponentiated regression parameter for the interaction term equals the ratio of the aOR for the outcome with helmet use for females over the same for males (aORR). Significance was defined as a two-sided p-value<0.05.
Results
Summary statistics for demographics and outcomes are given in Table 7. After excluding 62 records in which gender was not recorded, there remained 18,604 patient records, 18.0% of which were female. Conditions that might predispose to head bleeding were low; anticoagulant was lower in females than males (1.4% vs. 2.8%, p<0.001), and bleeding disorders were less than 1% for both genders. The majority of riders were white, and the proportion was higher among females (84.1% vs. 76.6%, p<.001). The second most
Feler | 28 common single racial identity was black, with a lesser proportion among females (5.2% vs. 10.7%, p<0.001).
Table 7: Summary Statistics of Female and Male Rider Demographics and Outcomes Female (N=3352) Male (N=15252) Total (N=18604) p value Age, years, mean (sd) 48.211 (15.901) 48.108 (16.242) 48.127 (16.181) 0.745 Race <0.001 American Indian 20 (0.6%) 85 (0.6%) 105 (0.6%) Asian 126 (3.8%) 377 (2.5%) 503 (2.8%) Black 172 (5.2%) 1593 (10.7%) 1765 (9.7%) Other 197 (6.0%) 1399 (9.4%) 1596 (8.8%) Pacific Islander 6 (0.2%) 35 (0.2%) 41 (0.2%) White 2763 (84.1%) 11423 (76.6%) 14186 (78.0%) Missing 68 340 408 Anticoagulant Use 48 (1.4%) 422 (2.8%) 470 (2.5%) <0.001 Bleeding Disorder 20 (0.6%) 90 (0.6%) 110 (0.6%) 0.964 Blood Alcohol Level Greater than 0.08 160 (10.0%) 1779 (20.2%) 1939 (18.7%) <0.001 Missing 1746 6463 8209 Helmet Use 1320 (39.4%) 5448 (35.7%) 6768 (36.4%) <0.001 Motor Vehicle Collision 1079 (32.2%) 6117 (40.1%) 7196 (38.7%) <0.001 GCS Mild [13-15] 3059 (95.1%) 13615 (92.3%) 16674 (92.8%) Moderate [9-12] 113 (3.5%) 826 (5.6%) 360 (2.0%) Severe [3-8] 43 (1.3%) 317 (2.1%) 939 (5.2%) <0.001 Missing 137 494 631 ISS Greater than 15 500 (14.9%) 3024 (19.9%) 3524 (19.0%) <0.001 Missing 5 24 29 Severe Head Injury Head AIS>3 550 (16.4%) 2791 (18.3%) 3341 (18.0%) <0.001 Missing 3 4 7 Skull Fracture 288 (8.6%) 1585 (10.4%) 1873 (10.1%) 0.002 Intracranial Hemorrhage 530 (15.8%) 2315 (15.2%) 2845 (15.3%) 0.356 Cervical Spine Fracture 171 (5.1%) 1172 (7.7%) 1343 (7.2%) <0.001 Hospital Admission 2735 (81.6%) 12274 (80.5%) 15009 (80.7%) 0.138 Cranial Surgery 169 (5.0%) 943 (6.2%) 1112 (6.0%) 0.012 Death 312 (2.0%) 43 (1.3%) 355 (1.9%) 0.003
Blood alcohol level was less likely to be tested (47.9% vs. 57.6%) and to be positive (9.8% vs. 20.4%, p<0.001) in females. A smaller proportion of females had accidents involving collision with a motor vehicle (32.2% vs 40.1%, p<0.001). Overall helmet use was 36.4% and was slightly greater in females (39.4% vs. 35.7%, p<0.001). As shown in Figure 4.4, helmet use appears to increase with age for both genders and then declines in old age, with a greater drop in females over 65 years old, among whom helmet use was lower than in males of the same age group (49.3% vs. 42.6%).
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Figure 4.4: Proportion of Helmet Use by Sex and Age
As reflected in summative measures of injury, females suffered less severe injuries and were more likely to use helmets. Mean injury severity score (ISS) was lower in females (9.1 vs. 10.6, p<0.001), as was the proportion of patients with ISS greater than 15 (14.9% vs. 19.9%, p<0.001). Similarly, the proportion of the lowest category [3-8] of the ED Glasgow Coma Scale was lower in females (1.3% vs. 2.1%, p<0.001). Regarding injuries, among females there were proportionally fewer severe head injuries (head AIS>3), skull fracture, cervical spine fractures, and deaths. There were similar proportions of intracranial hemorrhage and hospital admissions. Overall, the rates of severe head injury and mortality were 18.0% and 1.9% respectively.
As shown in
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Table 8, For both males and females, unhelmeted riders tended to be younger, less likely to be white, and associated with higher risk injury mechanism features. In unhelmeted patients for both genders, the proportion of white patients was lower and the proportion of black patients was higher. For both females and males, the average age those without helmets was higher than those with helmets. Anti-coagulant use was slightly lower among unhelmeted males (2.3% vs. 3.5%, p<0.001), but there was no difference among females. The proportion of blood alcohol level testing was not different in females based on helmet use, but those without helmets were more likely to have a positive result if tested (14.7% vs. 2.1%, p<0.001). Unhelmeted males were more likely to have their blood alcohol level tested (61.8% vs. 50.1%, p<0.001) and to have a positive blood alcohol result if tested (27.3% vs. 4.6%, p<0.001). Motor vehicle collisions were more common in unhelmeted riders for both females and males (38.2% vs. 22.9%, 47.4% vs. 27.1%, p<0.001 respectively).
Helmet use improved global measures of injury severity and rates of head injury for males, but had a less universal effect in females. In unhelmeted males, mean GCS was nominally lower (14.0 vs. 14.5), but the percentage of patients in the lowest GCS category was significantly higher (7.1% vs. 3.0%, p<0.001). In females, mean GCS was marginally lower in unhelmeted patients (14.3 vs. 14.6, p = .031), and the proportion of patents with the lowest category of GCS was higher (4.4% vs. 2.2%, p<0.001). For both females and males, the mean ISS was nominally lower in unhelmeted patients (9.0 vs. 9.5, 10.5 vs. 10.8, p<0.001), but the percentage of patients with ISS>15 was unchanged. In females, only the percentages of severe head injury (18.3% vs. 13.6%, p<0.001) and skull fracture (10.2% vs. 6.1%, p<0.001) were higher in unhelmeted patients, while cervical spine fractures were less frequent (4.5% vs. 6.1%, p = 0.042), and rates of intracranial hemorrhage, hospital admission, cranial surgery, and death were unchanged. In males, rates of all of these were higher in unhelmeted patients except for cervical spine fractures, which were lower in unhelmeted patients (6.4% vs. 9.9%).
Of note, when helmeted females are compared directly to helmeted males, the rates of these injuries were all similar with the exception of cervical spine fracture which was lower
Feler | 31 in females (6.1% vs. 9.9%) and intracranial hemorrhage, which was higher in females (15.1% vs. 11.1%, p<0.001).
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Table 8: Demographics and Outcomes Stratified by Sex and Helmet USe Female Male No Helmet Helmet No Helmet Helmet p value p value (N=2032) (N=1320) (N=9804) (N=5448) Age, years, mean (sd) 46.6 (16.6) 50.6 (14.5) <0.001 46.0 (16.3) 51.9 (15.4) <0.001 Race <0.001 <0.001 American Indian 17 (0.9%) 3 (0.2%) 68 (0.7%) 17 (0.3%) Asian 72 (3.6%) 54 (4.2%) 207 (2.2%) 170 (3.2%) Black 146 (7.3%) 26 (2.0%) 1440 (15.1%) 153 (2.9%) Other 147 (7.4%) 50 (3.9%) 1075 (11.3%) 324 (6.0%) Pacific Islander 5 (0.3%) 1 (0.1%) 23 (0.2%) 12 (0.2%) White 1605 (80.6%) 1158 (89.6%) 6736 (70.5%) 4687 (87.4%) Missing 40 28 255 85 Anticoagulant Use 30 (1.5%) 18 (1.4%) 0.788 230 (2.3%) 192 (3.5%) <0.001 Bleeding Disorder 13 (0.6%) 7 (0.5%) 0.688 54 (0.6%) 36 (0.7%) 0.395 Blood Alcohol Level <0.001 <0.001 Greater than 0.08 147 (14.7%) 13 (2.1%) 1654 (27.3%) 125 (4.6%) Missing 1032 714 3746 2717 Motor Vehicle Collision 777 (38.2%) 302 (22.9%) <0.001 4643 (47.4%) 1474 (27.1%) <0.001 GCS 0.004 <0.001 Mild [13-15] 1822 (94.2%) 1237 (96.6%) 8533 (90.2%) 5082 (95.8%) Moderate [9-12] 27 (1.4%) 16 (1.2%) 253 (2.7%) 64 (1.2%) Severe [3-8] 85 (4.4%) 28 (2.2%) 669 (7.1%) 157 (3.0%) Missing 98 39 349 145 ISS Greater than 15 304 (15.0%) 196 (14.8%) <0.001 1987 (20.3%) 1037 (19.0%) <0.001 Missing 5 0 21 3 Severe Head Injury <0.001 <0.001 Head AIS>3 371 (18.3%) 179 (13.6%) 2126 (21.7%) 665 (12.2%) Missing 3 0 4 0 Skull Fracture 207 (10.2%) 81 (6.1%) <0.001 1316 (13.4%) 269 (4.9%) <0.001 Intracranial Hemorrhage 331 (16.3%) 199 (15.1%) 0.347 1711 (17.5%) 604 (11.1%) <0.001 Cervical Spine Fracture 91 (4.5%) 80 (6.1%) 0.042 632 (6.4%) 540 (9.9%) <0.001 Hospital Admission 1654 (81.4%) 1081 (81.9%) 0.717 7810 (79.7%) 4464 (81.9%) <0.001 Cranial Surgery 103 (5.1%) 66 (5.0%) 0.929 704 (7.2%) 239 (4.4%) <0.001 Death 27 (1.3%) 16 (1.2%) 0.769 255 (2.6%) 57 (1.0%) <0.001
Figure 4.5: Histogram of Patient Age Stratified by Sex with Mean Ages Indicated with Vertical Bars
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Multivariate Analysis In female patient only model, helmet use was associated with a protective effect for severe head injury (aOR 0.73) and skull fracture (aOR 0.62), no change in the odds of hospital admission, cranial surgery, and death, and increased odds of cervical spine fracture (aOR 1.51). In the male patient only model, helmet use was protective against severe head injury (aOR 0.51), intracranial hemorrhage (aOR 0.58), skull fracture (aOR 0.37), cranial surgery (aOR 0.63), and death (aOR 0.42), did not impact the odds of hospital admission, and increased the odds of cervical spine fracture (aOR 1.61).
In the gender combined model, greater odds of morbidity were associated with male gender, collision with an automobile, and absence of a helmet. Female sex was associated with decreased odds of severe head injury (aOR 0.8), skull fracture (aOR 0.73), cervical spine fracture (aOR 0.67), cranial surgery (aOR 0.69), and death (aOR 0.42), but did not significantly change the odds of intracranial hemorrhage or hospital admission. Involvement with a motor vehicle was associated with increased odds of severe head injury (aOR 1.47), intracranial hemorrhage (aOR 1.39), skull fracture (aOR 1.49), cervical spine fracture (aOR 1.52), and death (aOR 3.8), had no effect on odds of cranial surgery, and decreased the odds of admission (aOR 0.83). Helmets were protective against severe head injury (aOR 0.48), intracranial hemorrhage (aOR 0.54), skull fracture (aOR 0.35), cranial surgery (aOR 0.61), and death (aOR 0.34), though they were associated with increased odds of cervical spine fracture (aOR 1.59).
Increased age was also correlated with more severe injury and outcomes. Compared to patients between ages 18 and 45, patients greater than 65 years old more likely to have ISS>15 (aOR 1.60, p<0.001), severe head injury (aOR 1.62, p<0.001), intracranial hemorrhage (aOR 1.86, p<0.001), cervical spine fracture (aOR 1.28, p=0.011), hospital admission (aOR 1.43, p<0.001), and death (aOR 3.88, p<0.001), though they were less likely to have skull fractures (aOR 0.75, p=0.002) and extended hospital stay (aOR 0.59, p<0.001). Compared to the youngest group, patients between ages 45 and 65 were more likely to have ISS>15 (aOR 1.357, p<0.001), severe head injury (aOR 1.28, p<0.001), cervical spine fracture (aOR 1.45, p<0.001), hospital admission (aOR 1.29, p<0.001), Feler | 34 unfavorable discharge (aOR 1.11, p = 0.02), and death (aOR 1.68, p<0.001). This group was less likely to have a cranial surgery (aOR 0.78, p<0.001).
Figure 4.6: Effects of Model Parameters on Outcomes
P-value a is indicated by the shape of the data marker. * indicates a value of p<0.05, and ** indicates a values of p<0.001
Helmet Sex Interaction In female patient only model, helmet use was associated with a protective effect for severe head injury (aOR 0.73) and skull fracture (aOR 0.62), no change in the odds of hospital admission, cranial surgery, and death, and increased odds of cervical spine fracture (aOR 1.51). In the male patient only model, helmet use was protective against severe head injury (aOR 0.51), intracranial hemorrhage (aOR 0.58), skull fracture (aOR 0.37), cranial surgery (aOR 0.63), and death (aOR 0.42), did not impact the odds of hospital admission, and increased the odds of cervical spine fracture (aOR 1.61).
As shown in Table 9, the qualitative difference in the effects of helmet use observed the gender-segregated models is quantitated in the exponentiated sex-helmet interaction parameter in the combined model. It demonstrates that there are significant differences in the adjusted odds ratios for females and males in protection against severe head injury (aORR 1.42), intracranial hemorrhage skull fracture (aORR 1.6), cranial surgery (aORR
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1.64), and death (aORR 2.47). There was no gender-based difference in helmet effect for cervical spine fracture or hospital admission.
Table 9: Helmet * Sex Interaction Effect on Outcomes
Discussion
This analysis provides insight into the ramifications of shifting rider demographics on outcomes in the event of a micromobility accident. The assumption that observed differences among riders of c-bicycles will generalize to riders of e-bicycles and e-scooters will be discussed, however these data suggest that the previously identified shift among SMP riders towards younger age and greater representation of females may decrease the average injury severity and mortality in this cohort.
Our results support that females were less prone to serious morbidity and mortality and that helmets offered females a lesser degree of risk reduction compared to males. The former finding is consistent with other reports which have shown decreased risk of injury or mortality in women,26,55,56 though some have shown null effect57, or even increased risk.58 This is a likely a multifactorial phenomenon, and explanations range from anatomical differences that may increase frailty or vulnerability to differences in on-road behavior59,60 that might change accident severity. In this study, BMI was included in the model but did not significantly predict outcomes, so physical frailty seems a less likely explanation. Regardless of the cause, it is the case that helmets should be designed to protect patients Feler | 36 in the circumstances under which they are injured independent of whether there is a gender-based difference in the nature of those circumstances.
The Efficacy of Helmets for Protection against Head Injury The protective effects of helmets identified in this study are consistent with previous research. In systemic review and meta-analyses (overlapping samples), pooled odds ratios for head injury range from 0.31 to 0.50 for head injury, .31 to .42 for serious head injury, and 0.27 to 0.35 for fatality.9,10,61,62 In comparison, aORs of 0.48 for severe head injury and 0.34 for death were calculated here. One of these studies also demonstrated a significant association with neck injury (pooled OR 1.28),62 but the largest of them added an additional 8 studies to this analysis and did not identify this effect. This last group also found that neck injury was rare in comparison to head injury, with a rate of 2.6% across all studies in comparison to 29.0% with head, 7.4% with serious head, and 21.9% with face injury.9 The data provided here confirm the former study with a calculated aOR of 1.59 for cervical spine fracture with helmet use, though the definition of ‘neck injury’ instead of the more specific cervical spine fracture used in this study creates important limitations in comparing these values.
Although the generally lower injury severity and frequency among females in this study likely contributed to the lesser risk reduction associated with helmets for females as there is ‘less room to improve,’ it remains concerning that there is a null effect for females in protection from intracranial hemorrhage and death in female-only logistic regression. Although an explanatory theory that the typical energy involved in a female bicycle accident due to risk aversive behaviors might fail to reach some threshold in which helmets are protective, especially in light of the decreased rate of motor vehicle collisions among females, it fails to explain the fact that there are higher rates of intracranial hemorrhage in females wearing helmets than males wearing helmets. Moreover, mechanism was included in the logistic regression to account for these differences. A sex-accident mechanism interaction term added to the multivariate model yielded no significant model parameters.
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An alternative explanation is that helmet design and testing may be biased towards male anatomy. This phenomenon would not be novel, as recent investigations of automobile safety have revealed increased risk of injury and death for female occupants, which some authors have posited are attributable to gender-based biometric variation that limit the efficacy of automobile safety features.63,64 One study reported a 47% greater odds of sustaining a severe injury for females when controlling for age, mass, BMI category, and accident characteristics, an effect which the authors attribute to a male-bias in vehicle safety testing standards which depend largely on a 50th percentile male accident test dummy and, to a lesser degree, a 5th percentile female accident test dummy–which is a scaled down version of the male model.65
When considering bicycle helmets, this differential anatomy may manifest in better or worse helmet fit. Proper helmet fit has been shown to be positively correlated with outcomes in children, with poorly fit helmets associated with almost twice the risk of head injury compared to well fit helmets.66 In one study of helmet fit, females were 1.9 times more likely to wear an incorrectly adjusted helmet. Though they were more likely to wear the correct size, the authors also showed that stability (resistance to rotation around the head) was dependent on proper adjustment but not on correct size.67 A study using MRI data to create parametrized head surface anatomy models demonstrated that the averaged anatomy of a male head is different than the averaged surfaced anatomy of female heads, with the greatest differences being in overall size and relative size of the supraorbital ridge. They also demonstrated that parametrized head forms created from MRI data that included male heads yielded models that fit female anatomy worse than models created from female-only MRI scans, with the greatest concentration of errors being in the foreheads. The corresponding effect was also true in males.68 Finally, a quantitative evaluation of helmet fit using 3D scanning of head anatomy identified that females had a lower quality of fit as assessed against a set of commercially available of helmets.69 In sum, it may be the case that females are at heightened risk of inferior helmet performance and that gender-specified design and testing may improve fit and therefore safety.
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Although historically it may have been argued that there was greater utility in focusing on the protection of male heads as a large majority of injured bicyclists—including in this study—are male, an evolving landscape of personal transport suggests urgent need to improve protection of female riders. New forms of personal mobility, especially those supported by SMPs, are growing in popularity, and early reports of injuries associated with their use have revealed higher proportions of female riders. Studies of e-scooter related injuries have demonstrated relatively increased rates of female patients: 35% of 103 in San Diego, CA;70 41% of 228 in Los Angeles, CA;71 55% of 190 in Austin, TX; and 50% of 50 in Salt Lake City, UT.72 Despite the dismally poor rate of helmet use reported in these studies—ranging from 0% to 4.4%—the use of a bicycle helmet is recommended by the U.S. Consumer Product Safety Commission,73 and, insofar as they will be used equally, a greater number of them will be worn by female riders. There is, therefore, an urgency to addressing sex-based differences in performance.
Injury severity based on vehicle ownership There may be a difference between the general population and SMP accidents involving motor vehicles. Martin et al. reported that 54/83 (65%) of collisions reported among riders of the Capital Bike Share in Washington D.C. between 2011 and 2013 involved a motor vehicle,27 a rate much higher than the one identified here (39%). Nonetheless, there were no fatalities in the study period, and there have been none to date. The mortality of riders who collided with a motor vehicle in this study was 3.4%, a rate which is obviously higher than among these SMP riders. One explanation is the difference in reporting mechanism and patient selection (trauma registry vs. all-comers reported by SMP operators), but it is also possible that the typical SMP collision is in some way less severe. Future studies should aim to identify vehicle ownership in clinical data in order to better understand this relationship and the other effects described here.
Injury patterns based on vehicle type Unfortunately, national registry-based data are not yet coded in such a way that micromobility vehicle type or ownership can be identified in the United States of America. Still there is emerging literature exploring traumatic injury based on vehicle type. Identified Feler | 39 studies of e-bicycle and e-scooter injuries are summarized with comparative values for c- bicycles from the 2017 National Trauma Database® in
Feler | 40
Appendix 2, whereas those presenting trauma patients in Table 10. In general, the rate of head and neck injury is high regardless of vehicle type and that, in those studies examining this effect, helmets were shown to be protective, despite widely variable demographics and geographies with possibly different road infrastructure and laws. As such, comparisons should be made with caution.
Table 10: Selection of Values from Studies of Traumatically Injured Micromobility Riders C-Bicyclea E-Bicycle E-Scooter United States of United States of Israel74 Switzerland75 Israel74 Americaa America70 Age (mean) 48 33b 64 18 37 Male 82.0% 83.1% 48.3% 77.8% 65% Helmet Use 36.4% - 13% - 2% Motor Vehicle 38.7% 32.6% 41.4% 30.2 - Collision Intracranial 15.3% - 17% - 18% Hemorrhage Traumatic Brain 8.1% 8.1% - 4.8% 16.5% Injury Mortality 1.9% 0.5% 5.5% 0% 0% adata from the 2017 United States of America National Trauma Registry, bEstimated with equal distribution assumption from histogram data.
Studies comparing multiple vehicle types suggest both differential risk and patient demographics based on vehicle characteristics (bicycle vs. scooter, motorized vs. non- motorized), the latter of which, absent multivariate analysis, confounds comparison. In general, riders of e-bicycles are older and more likely to have severe head injuries, and e- scooter riders are younger and less likely to have severe injuries, despite abysmally low rates of helmet use. As given in the table above, Siman-Tov et al. reviewed the national trauma registry in Israel and identified 663 riders of e-bicycles and 63 riders of e-scooters who presented to the hospital after traffic-related injury. For both groups, roughly 30% had collided with a motor vehicle and 10% had an ISS consistent with severe or critical injury. The rate of traumatic brain injury was 8.1% among riders of e-bicycles and 4.8% among riders of e-scooters. Of e-bicyclists, 0.5% died during their stay in the hospital, compared to 0 e-scooter riders. Helmet use was not available.74
Gross et al. performed a single-center study comparing 47 patients presenting after e- bicycle accident (including 3 pedestrians struck by e-bicycles) to concurrently presenting riders of c-bicycles and motorcycles and found that e-bicycle accidents incurred higher rates
Feler | 41 of intracranial bleeding (17%) than c-bicycles (0%) and motorcycles (4%). Helmet use rates in these cohorts were 27% for c-bicycles, 13% for e-bicycles, and 93% for motorcycles, which the authors postulate as mediator of the increased rate of intracranial bleeding among e-bicyclists. Of note, the mean age in the c-bicyclist cohort was significantly less than among e-bicycles. (15.4 vs 29.7, p<.001).76
Tan et al. examined the national trauma registry in Singapore to compare motorized and non-motorized micromobility vehicles of any kind. Riders of motorized vehicles were older with a median age of 34 compared to 25 among riders of non-motorized vehicles; 10% of riders of motorized vehicles were over the age of 60 compared to 2% of riders of non- motorized vehicles. Eighty-two percent of motorized vehicle riders used an e-scooter while 13% used an e-bicycle. Helmet use data were not commonly available. The authors calculated an adjusted odds ratio of 3.78 for severe injury (ISS>9) and 1.80 for hospital admission associated with use of a motorized vehicle.77
Of specific interest regarding neurotrauma among riders of e-scooters, a case series of 13 patients referred for neurosurgical evaluation over 1 year included 5 cases of traumatic subarachnoid hemorrhage, 1 epidural hematoma, 4 of skull fractures, 1 central cord syndrome, 1 lumbar compression fracture, and 1 death. Only the patient with a lumbar fracture required neurosurgical intervention.78
In short, these suggest that there may be differences in the patterns of injury and patient demographics associated with vehicle type, but the ability to directly compare groups between studies is limited. Although the reports of mortality are comparatively low for e- scooters in these studies, it is important to recall that there are few e-scooter cases present in these studies and there have been over 20 deaths associated with e-scooter use in the United States alone in a single year. Beyond heterogeneity of outcome reporting between studies, the importance of accident mechanism and the involvement of automobiles in injury severity57,79,80—likely mediated by local factors such as quality of infrastructure81–83 and normative on-road behaviors—and demographics of local riders complicate the comparison of injury characteristics documented at separate centers. The use of national
Feler | 42 registries reduces the impact of local variation, though future studies should aim to compare vehicle types within a single geographical environment.
Section Summary
• SMP riders may be younger and more likely to be female than community bicyclists. • Female riders suffer less severe injuries, but the risk reduction afforded by helmets is more limited in females compared with males. Older patients had more severe injuries and greater likelihood of death. Involvement in a motor vehicle collision increases injury severity and risk for death. • E-bicycles and e-scooters may have differential injury patterns compared to c- bicycles, but studies directly comparing vehicle types within the same environment are limited. Head injury is common regardless of vehicle type, and helmet use seems universally protective. Nonetheless there is evidence from trauma registries that motorized vehicles are associated with increased injury severity.
5 Behavioral differences potentiating high risk mechanisms
As the authors of the Cochrane review examining the impact of infrastructure on bicycle safety emphacize, the “installation of cycling infrastructure does not necessarily mean that it was used in the way it was intended and its installation may lead to other unexpected behavior that may result in changes in the risk of collision; for example, the speed of cyclists may have increased on reconstructed roads.”40 Intuitively, rider behavior on the road modulates risk of injury, and local conditions such as car traffic and infrastructure may influence that behavior. Although the assessment of those factors is beyond the scope of this research, differences in vehicle type and ownership may also be impactful and may help to explain some of the differences in injury described previously. Of particular concern are helmet use and behaviors that change the risk of collisions with a motor vehicle, two modifiable factors identified in the prior section as mediators of injury severity.
Vehicle Type Mediating Behavioral Variation Feler | 43
SMPs make available new vehicle types that may be less commonly encountered among personal vehicles. E-bicycles and e-scooters are common alternatives to c-bicycles in current SMPs.4 The theoretical benefits of such vehicles are the ability to complete longer journeys over more hilly terrain while carrying greater loads. One study in which participants with a mix of cycling experience were offered free e-bicycles and observed for changes in travel patterns demonstrated increases the number and length of trips taken by subjects both for commuting and leisure purposes, with a greater effect on females than males.84
New vehicle types may have different safety profiles. Riders have reported near-miss events due to unfamiliarity with the vehicle causing difficulty regulating speed and the similarity in appearance to c-bicycles, which in turn caused other users to underestimate the speed of the rider, though this does not necessarily constitute a violation of traffic laws.85 Some studies take advantage of GPS data from embedded sensors attached to vehicles and have generally found no differences in rates of incorrect crossing of intersections between c- bicycles and e-bicycles and only small differences in average speed. In one naturalistic study from Germany, video camera mounted on the handlebars of 88 riders on a mix of c-bicycles and e-bicycles recorded 6,213 events in which the rider approached an intersection requiring a stop at a red light; in 16.3% of cases, the rider ran the light, and there were no significant differences in this rate based on vehicle type.86 In another naturalistic study of a college-associated SMPs in the United States of America, the GPS data from a cohort of c-bicycles and e-bicycles were compared based on vehicle type, and the authors concluded that riders of the two vehicle types were equally likely to violate traffic laws at intersections with approximately 80% of stop-signs and 70% of red lights navigated without stopping (the actual rate depends on the threshold speed for ‘stopping’ as the authors allowed for ‘rolling stops’ as acceptable behavior). This same study showed that e-bicycle riders sustained greater average speeds and c-bicycle riders on roadways (8.3mph vs. 6.6mph, p<.05) but that the opposite was true on pedestrian paths (6.9mph vs. 7.9mph, p<.05).87
Observational studies have also been performed with more mixed findings. Several studies performed in China have demonstrated similarity between c-bicycles and e-bicycles in a Feler | 44 variety of safety behaviors when controlling for age and gender. Wu et al. demonstrated no significant differences in correct navigation of red-lights,88 and Bai et al. showed the same for wrong-direction riding, riding in motorized lanes, and stopping beyond the stop line in another.89 However, in another study, e-bicycle riders were found to be 1.4 times more likely to demonstrate risky behavior crossing an intersection than c-bicycle riders, with running a red light being the most common. They were 2.5 times as likely to be involved in a traffic conflict, and 3 times as likely to be at fault for that conflict compared to c-bicycle riders, though no multivariate analysis was performed in this study.89 This is an important limitation, as in the Wu et al. study mentioned above, it was only after controlling for other variables that vehicle type became a nonsignificant factor.88
Similar data about the on-road behaviors of e-scooter riders from observational or naturalistic studies is unfortunately lacking. One study of riders in Santa Monica, CA reported rates of 94.3% for helmet non-compliance, 7.8% for tandem riding, 26.4% for sidewalk riding, and 9.3% for traffic law non-compliance (unspecified).
In general, compliance with laws is highly variable, but, based on these data, it does not seem dependent on vehicle type.
Vehicle Ownership Mediating Behavioral Variation
Several studies have examined the effect of vehicle ownership on safety behaviors with most showing only marginal differences apart from comparatively low rates of helmet use and a greater presence of vehicle lights for night-time riding among SMP riders. Wolfe et al. found that vehicle ownership did not impact rates of riding in bicycle lanes, yielding to pedestrians, or obeying traffic signals. Helmet use was significantly higher among riders of personal vehicles (77% vs. 39%, p =0.0001). Whereas only 39% of personal vehicles had reflectors/lights, 100% shared vehicles had these features as they were built into the vehicle (p=0.0001).90 An observational study of riders of shared and personal bicycles in China estimated that former travelled somewhat more slowly (7.6 mph vs 8.7 mph average, respectively)91 In a study of riders in New York City, shared bicycle riders were more likely to be wearing headphones (4.6% vs. 2.4%, p<0.0001) but were less likely to talk on the Feler | 45 phone (0.38% vs. 0.24%, p=0.33) or ride while looking down at a phone (0.33% vs. 0.37%, p=0.78).92 An observational study in Canada demonstrated that bikeshare riders had similar rates of near-miss in busy intersections.93
Although compliance with traffic laws may not is not obviously dependent on vehicle ownership, poor helmet use has been consistently demonstrated among riders of shared vehicles in numerous studies across several locations, with a summary of their findings given in Table 5.1.
Table 5.1: Helmet Use Among Riders of Shared and Personal Bicycles Helmet Use Helmet Use Shared Personal Authors City Year Shared (%) Personal (%) (n) (n) Mooney et al.94 Seattle 2019 20 91 59 952
Zanotto & Winters.95 Vancouver 2017 64 78.6 397 10,704
Ethan et al.92 NYC 2016 27.5 59.5 2,105 14,529
Wolfe et al.90 Boston 2016 39 77 122 1563
Basch et al.96 NYC 2014 14.7 - 1,054 -
Bonyun et al.52 Toronto 2012 20.9 51.7 306 6,426
Fischer et al.97 Boston/DC 2012 19.2 51.4 562 2511
Kraemer et al.53 DC 2012 33.1 70.8 142 1,268
The variance in helmet use between these studies highlights the importance of controlling for location in comparing vehicle types. For this reason, an observational study of safety behaviors was completed in San Francisco, where all major vehicle types were contemporaneously deployed.
Methods
Study Design We conducted a prospective, observational study of bicycle and scooter riders in the city of San Francisco, California between 2/20/2019 and 3/20/2019. Conventional scooters were not included because they are not available for rent through SMPs. Observations were performed by a team of four researchers, each of whom was trained by the lead researcher
Feler | 46 to systematically collect data. Exempt approval was granted through the Institutional Review Board of our institution.
Observation Sites Seven intersections were purposively sampled for observation during two-hour, peak commute time in the morning (7AM – 9AM) and evening (5PM – 7PM) on days without precipitation. Observation sites shown in Error! Reference source not found. were chosen to maximize the number of riders, maximize the number of SMP riders, and minimize repeated sampling of the same rider in multiple observations. High volume intersections were selected based on publicly available bicycle-traffic volume data, public United States census data indicating job density, the locations of SMP docks, and the locations of dockless vehicles as indicated by their respective smartphone applications. Three of the observations were carried out at stop-sign intersections, and four were carried out at stop light intersections. Only intersections at the junction of two officially designated bicycle corridors - defined as roads with bicycle-specific infrastructure such as a shared lane, bike lane, or separated lane - were considered. No single bicycle corridor was represented at more than one observation site to minimize duplication of riders. Each site was visited by the research team before observations were made to ensure the presence of a safe location with clear sightlines from which to observe the intersection.
Figure 5.1: Observation Sites and Job Density
Observation Procedures
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Each observation was carried out over two hours by two researchers. Researchers were trained to identify vehicle type and ownership status by reviewing representative images of the vehicles, and each practiced live coding for 20 minutes before their first observation. With two researchers at each observation, each was responsible for documenting riders approaching from two directions to the intersection. As a rider entered the intersection, appropriate codes were entered by the observer into an Excel spreadsheet with a time stamp containing the hour and minute of arrival. Riders walking alongside their vehicles were not included.
Observational Coding A codebook was generated to characterize the vehicle type, ownership status, distribution model, and helmet use of observed riders. ‘Vehicle types’ included conventional bicycles c- bicycles, e-bicycles, and e-scooters. ‘Ownership status’ included shared and personally owned vehicles. For shared vehicles only, ‘distribution model’ was defined as either station- based, meaning they had to be checked out and returned at a designated station, or dockless, meaning they could be checked out and returned anywhere within a designated area. Personally owned vehicles did not have an associated distribution model.
Vehicle types and ownership status were identified by distinctive markings, including company logos, batteries, and motors. Distribution model was inferred from the company logos as each SMP company at the time of coding employed only one model of distribution. Helmet use was coded as ‘yes’ or ‘no’; riders holding a helmet or wearing an unsecured helmet were counted as ‘no.’ At stop signs, riders that came to a complete halt and placed their foot on the ground were identified as having stopped appropriately. At stop lights, riders that did not enter the intersection when the light was red and the walk sign was not illuminated were identified as having stopped appropriately. Any rider whose wheels entered the elevated pedestrian sidewalk (excluding the crosswalk within the street) was identified as having ridden on the sidewalk.
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Data Analysis Frequency statistics were calculated to determine overall and location-specific prevalence of riders by vehicle type, ownership status, and helmet use. Welch’s t-test was used to compare differences between shared and personal vehicle types, and to compare differences in helmet use between vehicle types and distribution models. The chi-square test was used to compare overall differences in the distribution of vehicle type and ownership status.
A multivariate generalized linear mixed-effects model accounting for random effects at each location was used to calculate adjusted odds ratios for helmet use and safety behaviors by vehicle type and ownership. Additionally, helmet use was included as an independent variable in the multivariate models of safety behaviors. A subgroup analysis comparing helmet use among station-based and dockless shared e-bicycles was performed by using Welch’s t-test with significance threshold adjusted with the Bonferroni Correction for multiple comparisons and calculating an adjusted odds ratio from the linear mixed effects model, accounting for different random effects at each location. This subgroup was selected because all shared e-scooters were dockless and all shared c-bicycles were station-based, preventing comparison of the distribution model. Data were analyzed using R Studio (Version 1.1.463). Significance was defined as a two-sided p-value less than .05.
Results
During the study period, a total of 4472 riders were observed riding c-bicycles, e-bicycles, and e-scooters. The majority of riders (80.5%) used personal vehicles, mostly c-bicycles (89.8%), whereas most riders of shared vehicles rode e-bicycles (78.5%) (Table 5.2). Comparing shared to personal vehicles observed, there were greater proportions of e- scooters (7.8% vs. 3.7%, p<.001) and e-bicycles (78.7% vs. 6.5%, p<.001).
Table 5.2: Vehicle Type by Ownership Status c-bicycle e-bicycle e-scooter All Vehicle (% by (% by (% by Types ownership) ownership) ownership) Both Personal & 3350 920 202 4472 Shared (74.9%) (20.6%) (4.5%)
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3232 233 68 Personal 3599 (89.8%) (6.5%) (3.7%) 118 687 134 Shared 873 (13.5%) (78.7%) (7.8%)
As shown in Table 5.3, of all riders, 74.5% wore helmets, but helmet use was significantly higher among riders of personal vehicles of all types than among riders of shared vehicles of all types (84.6% vs. 37.3%, p<.001). Ignoring vehicle ownership, the frequency of helmet use was also significantly higher among riders of c-bicycles than of e-bicycles and e- scooters, but there was no significant difference between riders of e-bicycles and e-scooters. The results of the Tukey test suggest that each subgroup had a different proportion of helmet usage from all other groups with the following exceptions: personal c-bicycles compared to personal e-bicycles and shared c-bicycles compared to shared e-bicycles yielded no differences in the proportion of helmet use. When stratified by ownership, rates of helmet use were not significantly different between riders of c-bicycles and e-bicycles, but riders of e-scooters were significantly less likely to use a helmet than riders of both e- bicycles and c-bicycles in both the shared and personal ownership groups.
Table 5.3: Helmet Use Among Riders of Personal and Shared Vehicles
All Vehicle Types c-bicycle e-bicycle e-scooter
Both Shared and Personal 75.4% 84.1% 50.9% 42.6% Personal Only 84.6% 85.7% 86.3% 55.2% Shared Only 37.3% 39.8% 38.9% 17.6%
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Figure 5.2: Proportion of Helmet Use by Vehicle Type and ownership
The multivariate model of helmet use (Table 5.4) that included observation location, vehicle ownership, and vehicle type showed that riders of e-scooters were less likely to use a helmet than riders of c-bicycles. Helmet use did not differ significantly between riders of e-bicycles and c-bicycles. Riders of shared vehicles were less likely to wear a helmet than riders of personal vehicles.
Table 5.4: Multivariate Analysis of Helmet Use aOR (95%CI) p-value Vehicle Type c-bicycle Ref - e-bicycle 1.0 (0.74 – 1.25) 0.76 e-scooter 0.24 (0.16 – 0.31) <.001 Ownership Personal Ref - Shared 0.11 (0.08 – 0.14) <.001
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The subgroup analysis comparing helmet use among station-based and dockless shared e- bicycles showed that of all shared e-bicycles observed, 77.9% were station-based and 22.1% were dockless. The two groups differed significantly in helmet use (44.5% versus 25.6%, p<.001). When observation location was controlled for, riders in the dockless group were less likely to wear a helmet than riders in the station-based group (OR = 0.44, 95% CI: 0.29 - 0.65, p<.001). Notably, the rate of helmet use did not differ significantly between dockless shared e-bicycles and shared e-scooters, all of which were dockless (25.6% vs. 17.6%, p = 0.25).
There were significant differences in safety behaviors based on vehicle type but not based on ownership. The results of the multivariate models of safety behaviors are given in Table 5.5. These results suggest that riders of e-scooters are more likely to stop completely at intersections.
Table 5.5: Multivariate Analysis of Safety Behaviors by Vehicle Subtype Wait at Stoplight OR Stop at Stopsign Riding on Sidewalk OR Group (95%CI, p) OR (95%CI, p) (95%CI, p) (n = 2083) (n = 1831) (n = 4472) Vehicle Type C-Bike Ref Ref Ref E-Bike 1.2 (.79–1.84, p=.39) 1.43 (.70–2.83, p=.32) 1.20 (.79–1.84, p=.39) E-Scooter 1.78 (1.03–3.25, p<.05) 4.34 (1.99–8.76, p<.01) 1.81 (1.23–2.33, p<.05) Ownership Status Personal Ref Ref Ref Shared .96 (.62–1.50), p=.87) 1.57 (.80–3.07, p=.19) 1.01 (.55-1.54, p=.97) Helmet Yes Ref Ref Ref No 1.09 (.82–1.46, p=.57) .67 (.45-.97, p<.05) 1.09 (.81–1.46, p=.57)
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Discussion
This prospective observational study conducted in an urban setting sought to determine differences in helmet use between riders based on the type of vehicle—e-scooter, c-bicycle or e-bicycle—and whether the vehicle was personally owned or shared. Riders of shared vehicles were less likely to wear a helmet than riders of personal vehicles, and more likely to use an electrically motorized vehicle. Riders of e-scooters were less likely to wear a helmet than riders of c-bicycles and e-bicycles regardless of ownership. Considering shared e-bicycles only, riders of dockless vehicles were less likely to wear a helmet than riders of station-based vehicles.
In this analysis, 37.3% of riders of shared vehicles used helmets, which is notably higher than the rates found by studies performed in Toronto (21%),98 Boston and Washington DC combined (19%),97 New York City (15%)99, and Seattle (20%).100 Higher rates than ours were reported in a study of Boston only (39%)90 and Vancouver (64%). Interestingly, in Vancouver, helmets were freely available at the site of rental.101 The variation in helmet use among cities by shared vehicle riders suggests that there are regional differences in helmet use. Based on the findings of this study, factors contributing to these differences may be the vehicle types and distribution model provided by local SMPs.
The SMP distribution model may also affect rider safety. The findings of this analysis suggest that riders of dockless vehicles are less likely to wear a helmet. Although we could only analyze this effect directly among riders of shared e-bicycles, it may have contributed to the low rate of helmet use among riders of shared e-scooters, all of which were dockless vehicles. This effect has not been described previously in the literature, but the shared bikes observed by Mooney et al. in Seattle were all dockless, and their rates of helmet use were comparable at 20% (vs. the 25.6% described in this study). We speculate that, because dockless vehicles might be encountered anywhere in a city, they may be more likely to inspire a spontaneous trip. In contrast, the predictability in finding a station-based vehicle in a known location may attract riders who plan their trip and may be more likely to carry
Feler | 53 a helmet. Regardless of the cause, poor helmet use in all SMP cohorts may ultimately offset benefits to injury severity associated with a younger and more female ridership.
The observation that riders of e-scooters are more likely to stop appropriately at intersections, more likely to ride on the sidewalk, and less likely to wear a helmet than riders of c-bicycles and e-bicycles suggests that riders of these vehicles demonstrate an entirely different profile of safety behaviors compared to riders of bicycles. It is possibly the case that riders of e-scooters do not feel safe or welcome riding in ‘bicycle lanes.’ Another possible explanation of this set of behaviors is risk mitigation, specifically avoiding the high-risk mechanism of being struck by a vehicle as a compensation to not wearing a helmet (notwithstanding that a literature review suggested that riding on the sidewalk paradoxically increases the risk of bicycle accident102–104). This phenomenon has been examined among riders of personally owned c-bicycles and e-bicycles by attempting to correlate travel speed at the time of accident with helmet use using naturalistic methods. The authors refuted the notion that a cyclist would ride at a slower speed when not wearing a helmet.105 Within data collected in this study, this model is made less likely by the fact that in a multivariate model including vehicle type, helmet use did not predict sidewalk riding. Rather, this difference was mediated entirely by vehicle type, supporting the notion that riders of e-scooters behave differently for reasons associated with their vehicle type alone.
The definitions of appropriate stopping behavior used in this study may impact the interpretation of findings. Concerning stop signs, the requirement that a rider place a foot on the ground excludes riders that significantly reduce their speed (but do not come to a complete stop) to allow other road users that have already arrived to the intersection to pass through first. This choice was made deliberately as study personnel observed many near- miss collision events that followed this particular behavior, and it was deemed an unsafe practice. We hypothesize that by placing their foot on the ground, riders clearly indicated their intent, allowing others at the intersection to plan their actions more appropriately, avoiding these near miss events. One criticism of this definition is that it favors riders of e- scooters, for whom it is simpler to place their foot on the ground compared to c-bicycles Feler | 54 and e-bicycles on account of their standing position. By way of defense, this may alternatively be perceived as a relative benefit provided by this vehicle type in terms of supporting safe use: if it is more comfortable to stop completely, perhaps more riders will choose to do so.
Regarding behavior at stop lights, the tolerance for riders to enter the intersection after the walk sign was illuminated but prior to the traffic light turning green followed from conversation with local traffic police, who indicated that this behavior was not manifestly illegal and was the de facto law for appropriate and safe behavior of bicyclists and scooter riders at intersections with traffic lights and walk signals. On discussion, study personnel agreed that this behavior did not precipitate near miss events even though it created a hypothetical risk for collision with a car moving in the crossing direction that passes through the intersection after their light turns red.
This study had several limitations. First, the purposive sampling method used to select study sites was intended to increase the relative frequency of commuters, a population that may have different helmet-use patterns than riders who use vehicles for other purposes,90 including short trips during the work day, recreational riding, and tourism and may undersample e-scooters.4 Second, the same riders may have appeared in multiple observations although we attempted to mitigate this by selecting sites that represented distinct commuter corridors. Third, these data do not account for helmet use during inclement weather as precipitation was a study exclusion criterion. Because the study was conducted from 2/20/2019 and 3/20/2019, seasonal variation was not assessed. Finally, rider gender was not collected, and, as discussed previously, may be an important factor in rider behavior and injury pattern.
Section Summary
• Riders of shared vehicles are more likely to use an electrically motorized vehicle but less likely to use a helmet.
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• Decreased helmet use is associated with riding a shared vehicle, riding an e-scooter, and riding a dockless rather than a station-based e-bicycle. Poor helmet use may offset benefits to injury severity from decreased age and female representation. • Vehicle type mediates changes to on-road behavior. Riders of e-scooters are more likely to ride on the sidewalk and more likely to stop correctly at intersections than riders of c-bicycles and e-bicycles. There were no apparent differences in the behavior of c-bicycle and e-bicycle riders.
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6 Conclusion
SMPs represent an increasingly common urban transit option throughout the United States with important impact on the epidemiology of traffic-related trauma. SMPs do not appear to increase or decrease rates of death of bicycle riders in urban settings; however, deaths of riders of non-bicycle micromobility vehicles are not easily tracked in national databases. This is especially concerning as, where bicycle SMPs have been associated with only 3 deaths in over a decade of use while e-scooter SMPs have been associated with over 20 in just two years. The effects of these new forms of SMP on rates of death remain to be seen. The trend towards a younger and more female ridership will likely decrease the typical injury severity and mortality of SMP associated trauma, however poor helmet use among the SMP riders may offset these differences. Both helmet use and compliance with traffic laws were associated with vehicle type; SMPs may therefore a mechanism for modulating rider behavior. Further research is required to better understand and optimize these differences in behavior to improve the safety of urban transport.
Our findings have important implications for safety research and mobility infrastructure planning as they relate to the deployment of SMPs. Policy makers and departments of transportation should consider the potential safety implications of distribution models and vehicle types of competing SMPs in selecting between programs for their jurisdictions. SMP vendors may have a role in improving public safety by optimizing their programs to promote safe use. Future research must address differences in circumstances of vehicle use, such as trip purpose and timing, demographics of riders, travel patterns, risk-perception and on-road behavior. Specific concerns are intoxicated riding due to the availability of SMPs in nightlife areas, increases in use by very young or very old people, and differences in how riders navigate the environment that might induce more accidents or cause more severe injuries, such as head-on collision.57,106 The influence of SMPs on individual’s feelings towards using helmets should also be explored because the perception of normative helmet-wearing behavior may influence riders’ helmet use.107
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Finally, changes to the epidemiology of micromobility-associated trauma must be kept in context of traffic-related injury in general including pedestrian and motor vehicle trauma. The goal for many of these programs is to decrease the overall rate of traffic-related injury and death by decreasing motor-vehicle congestion in urban centers. For that reason, a careful balance must be struck between creating services that are appealing to potential riders and adequate regulation and safety features to ensure maximal rider safety.
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7 Bibliography
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8 Appendices
Appendix 1: Cities in FARS Regression
2010 Census Population SMP Introduction Date City SMP Introduction Date Estimate Source
http://citibikenyc.com/assets/pdf/12-
New York city, New York 8,174,988 5/27/2013 042_bike_share_launch.pdf
https://www.dailynews.com/2016/07/07/los-angeles- Los Angeles city, California 3,792,820 7/7/2016 finally-has-a-bike-sharing-program-and-they-have-big- plans-for-it/
https://www.chicago.gov/city/en/depts/cdot/provdrs/bik Chicago city, Illinois 2,695,624 6/28/2013 e/news/2013/jul/chicago_welcomesdivvybikesharingsyste m.html
https://www.houstontx.gov/planning/transportation/BC
Houston city, Texas 2,093,615 12/31/2013 ycle.html
Philadelphia city, https://www.centreforpublicimpact.org/case-
1,526,009 4/23/2015 Pennsylvania study/philadelphias-indego-bike-sharing-system/ https://www.azcentral.com/story/news/local/phoenix/20 Phoenix city, Arizona 1,446,914 11/25/2014 14/11/25/phoenix-launches-grid-bike-share-- systen/70094380/
https://www.mysanantonio.com/news/local_news/article
San Antonio city, Texas 1,326,768 3/26/2011 /Bicycle-sharing-launched-in-S-A-1308451.php
https://www.kpbs.org/news/2015/jan/30/first-san-
San Diego city, California 1,301,949 1/30/2015 diego-bike-share-stations-open-business/
https://www.dallasobserver.com/news/dallas-unveils-
Dallas city, Texas 1,197,653 11/13/2014 worlds-saddest-bike-sharing-program-7129476
https://www.sfgate.com/bayarea/article/Bay-Area-Bike-
San Jose city, California 952,060 8/29/2013 Share-program-about-to-begin-4769703.php
https://www.actionnewsjax.com/news/local/bike- Jacksonville city, Florida 821,764 4/5/2017 sharing-kiosks-coming-to-jacksonville- beach/509385645
Indianapolis city (balance), http://www.ibj.com/bike-sharing-lined-up-for-cultural-
820,436 4/22/2014 Indiana trail/PARAMS/article/41876 https://www.sfgate.com/bay area/article/Bay-Area-Bike- San Francisco city, California 805,184 8/29/2013 Share-program-about-to- begin-4769703.php
http://www.kxan.com/news/local/austin/bike-share-
Austin city, Texas 802,078 12/21/2013 program-to-launch-next-month
http://www.columbusunderground.com/locations-set- Columbus city, Ohio 789,011 7/30/2013 for-cogo-bike-share-system-mid-summer-launch- planned-bw1 http://www.fortworthbikesh Fort Worth city, Texas 744,852 4/22/2013 aring.org/
Charlotte city, North http://pundithouse.com/2011/12/pedaling-push-for-
735,692 7/12/2012 Carolina dnc-2012/
https://www.mlive.com/news/detroit/2017/05/mogo_lau
Detroit city, Michigan 713,885 5/23/2017 nch.html
http://www.elpasotimes.com/news/ci_28612586/el-
El Paso city, Texas 648,254 9/16/2015 pasos-bike-share-program-launching-september
https://www.memphisdailynews.com/news/2017/jun/9/e xplore-bike-share-to-bring-600-bike-system-to- Memphis city, Tennessee 651,885 5/23/2018 memphis/?utm_source=feedburner&utm_medium=feed &utm_campaign=Feed:+memphisdailynews/bbde+(The+
Memphis+Daily+News))In http://www.baltimoresun.com/news/maryland/baltimore -city/bs-md-ci-bike-share-contract-20160314- Baltimore city, Maryland 620,862 10/28/2016 story.html)(http://www.bizjournals.com/baltimore/news/ 2016/08/19/baltimores-first-bike-share-rentals-coming-
in.html?ana=fbk)
Boston city, Massachusetts 617,786 7/28/2011 https://www.bluebikes.com/about
https://seattle.curbed.com/2014/10/14/10035888/seattle Seattle city, Washington 608,666 10/13/2014 -celebrates-the-opening-of-its-firstever-bike-share- program Washington city, District of 601,766 8/2/2008 https://www.capitalbikeshare.com/about Columbia Nashville-Davidson http://www.newschannel5.com/story/19158880/mayor-
metropolitan government 603,427 12/13/2013 dean-unveils-new-bike-sharing-program (balance), Tennessee
https://www.fcgov.com/transportationplanning/pdf/fc_b
Denver city, Colorado 599,815 4/22/2010 ike_share_business_plan_final.pdf Louisville/Jefferson County http://wfpl.org/public-bike-share-could-roll-into-city-
metro government (balance), 595,386 5/25/2017 by-summer-officials-say/ Kentucky Feler | 69
http://www.bizjournals.com/milwaukee/news/2014/08/0 Milwaukee city, Wisconsin 594,511 12/31/2015 6/milwaukee-bikeshare-program-expands-gets-a-name- of.html?page=all
http://www.oregonlive.com/commuting/index.ssf/2016/
Portland city, Oregon 583,792 7/19/2016 01/nike_to_sponsor_portlands_bike.html
http://www.lasvegasnow.com/news/rtc-to-launch-bike- rental-program- Las Vegas city, Nevada 584,509 9/30/2016 downtown)http://www.reviewjournal.com/news/las- vegas/rtc-oks-contract-bike-sharing-program-
downtown-las-vegas
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Appendix 2: Epidemiological Studies of E-Bicycles and E-Scooters
Study Year Pub Setting children) n ( n Age Mean %Mortalty Effect Helmet Notes Male % ISS Injuries Head Reported Car by Struck % %Confirmed Intoxicated Helmet %Confirmed
Single Center 72
Emergency Major Head Injury: skull fracture 8% Major, 12% Department, 50 (0) 34 50% - 0% - 16% 0 - or ICH; Minor Head Injury 2019 Minor,<=8% ICH United States of (closed head injury/concussion) Badeau et al. et Badeau America
108 Single Center Emergency 18.5% minor, 0% Helmet reduces head 54 (0) 30.9a 51.90% - 0% - 27% 46% 1 pedestrian included 2019 Department, ICH injury OR .18
Mitchell et al. et Mitchell Australia
78
. 76.9% body area,
Neurosurgical
et al et 46.2% ICH (4 tSAH, Consults, United 13 (0) 44.5 61.50% - 7.70% - - 2019 1 tSAH + EDH, 1
Schlaff Schlaff States of America tSAH + SDH)
70 15% headAIS>=3 and
Multi Center
no surgery. 16.5% Trauma Registry, 103 (0) 37.1 65% 5.5 concucssion w/o ICH, 0.00% - 48% 2% - scooter 2019 - United States of E 18% ICH (4 SDH, 10
Kobayashi et al. et Kobayashi America SAH, 6 IPH)
109 Minor head injury: concussion, Single Center 29% TBI, open wound of head; head
Emergency drank 48% body area, 15% injury undefined. 39% injured Department, 190 (9) median 29 55% 0.00% 10% alcohol <1% 2019 TBI between 6pm and 6am. United States of within 12 Calculate 20 injuries per 100,000 America hous prior Hayden and Spillar and Hayden e-scooter trips
Single Center TBI: concussions and other 71
Emergency forms of altered mental status or none of ICH patients Department, 228 (26) 33.7 58.9% - 38.2 minor, 2.2% ICH 0% 8.8% 5.20% 4.40% bleeding such as subarachnoid 2019 wore a helmet United States of hemorrhage or subdural Trivedi et al. et Trivedi America hematoma
74 . et al et National Trauma 63 (32) 18 77.8% 11.1%>15 36.5% area, 4.8% TBI 0% 30.2% - - - - 2017 Registry, Israel Siman Tov Siman
76 Admitted Patients, 9% undefined head includes 6.3% pedestrian (1 of 47 (17) 29.7 87.2% 12.1 2.10% 25% - 13% 2018 Israel injury, 17% ICH whom died) Gross et al. et Gross
Bicycle - significantly reduces
E 75 . head an neck injury, et al et 36.2% SAH, 25.9% Trauma Registry, subdural bleeding, 58 (0) 64.3 48.3% - SDH, 5.2% EDH, 5.50% 41.4% - 1.70% 2018 Switzerland intracerebral bleeding, 12.1% IPH
de Guerre Guerre de skull fractures, and skull base fractures
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110 . Single Center
et al et Pediatric 40% body area, 1.1% 85 (85) 13.7 67% 3.1 0% - - 21% - 2018 Emergency ICH
Hermon Hermon Department, Israel
74 . et al et
25 (33 after National Trauma 8.1% TBI, 44.5% body 795 (215) excluding 83.1% 0.50% 32.6% - - 2017 Registry, Israel area children) Siman Tov Siman
111 Single Center Emergency 14.9% mild brain injury, 23 (0) 47.5 70% 8.48 0% 17.4% 8.7 - - 2014 Department, 7.4% SAH
Papoutsi et al. et Papoutsi Switzerland
112
Single Center 46.4% body area, Admitted Patients, 323 (14) 43.8 52.3% 0%* 48.6% - - - *excluded 7 fatalities from analysis 2014 35.9% TBI Du et al. et Du China
7.5% TBI, 16.8%
Concussion, 18%
Trauma Registry, Severe Head Injury,
Bicycle United States of 18608 (0) 48.1 84.1% 10.35 1.9% 38.7% 11.8% 34.2% 2017 -
Registry 15.3% Any ICH, C America 10.7% SAH, 9.2% National Trauma Trauma National SDH, 1.4% EDH a estimated from histogram data with assumption of uniform distribution within strata.
Appendix 3: ICD10 Code Dictionary for National Trauma Registry® Analysis
Diagnosis Codes Collision with Motor Vehicle E-Code V12., V13., V14., V15., V19.0, V19.1, V19.2, V19.4, V19.5, V19.6 non_mvc_codes V10., V11., V16., V17., V18., V19.4, V19.8, V19.9 Skull Base Fracture S02.0 Skull Vault Fracture S02.1 Concussion S06.0 Diffuse Traumatic Cerebral Edema S06.1 Diffuse TBI S06.2 Focal TBI S06.3 Epidural Hematoma S06.4 Subdural Hematoma S06.5 Subarachnoid Hemorrhage S06.6 Other Intracranial S06.8, S06.9
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