10937_Shifts In Micromobility-Related Trauma In The Age Of Vehicle Sharing The Epidemiology Of Head Injury

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January 2020
Shifts In Micromobility-Related Trauma In The Age Of Vehicle
Shifts In Micromobility-Related Trauma In The Age Of Vehicle
Sharing: The Epidemiology Of Head Injury
Sharing: The Epidemiology Of Head Injury
Joshua Richard Feler
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Recommended Citation
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

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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

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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
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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
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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
E-bicycle
Jump
20 mph
78 lbs
250 W
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
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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
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.
0
100
200
300
400
500
600
700
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
Rural
Urban
Linear (Rural)
Linear (Urban)
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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
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rate of bicycle commuting in each city’s population by the formula proposed by Barnes and
Krizek22:
3% + % bicycle commuting
100
× population × 365
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 | 12 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 Feler | 13 (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 | 14 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 person years) CRPT (deaths per 100,000,000 trips) 100 Million Trips (n) 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, Pennsylvania 0.10 0.16 64.9 4.4 7.1 60.9 0.34 0.35 3.6 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), Indiana 0.18 0.41 129.0 13.3 29.9 125.0 0.11 0.12 3.9 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 Carolina 0.13 0.19 40.0 11.4 15.2 33.8 0.09 0.10 12.1 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 | 15 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 Columbia 0.09 0.17 91.3 4.1 7.5 81.7 0.12 0.13 10.1 Nashville-Davidson metropolitan government (balance), Tennessee 0.08 0.00 -100.0 6.6 0.0 -100.0 0.08 0.08 2.9 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), Kentucky 0.32 0.97 198.0 24.7 72.8 195.0 0.08 0.04 -49.1 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. Feler | 16 Table 3.3: Effects of Bikesharing on CRPP, CRPT, and Trip Volume Univariate Multivariate Trip Volume 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 | 17 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 | 18 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 City Date Trips Prior Worst Case SMP CRPT Estimated CRPT 2 years 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 | 19 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 Feler | 20 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 | 21 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. Feler | 22 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. Feler | 23 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 Data from NHTSA Fatal Accident Reporting System14 R² = 0.9796 0 10 20 30 40 50 60 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Feler | 24 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.