Traumatology

National analysis of motorcycle associated injuries and fatalities: Wearing helmet saves lives

a b s t r a c t

Introduction: Riding a motorcycle without a helmet represents a public health risk that can result in disabling injuries or death. We aim to provide a comprehensive analysis of the impact of helmet use on motorcycle injuries, injury types, and fatalities, to highlight areas requiring future intervention.

Methods: We performed a retrospective cohort study utilizing the American College of Surgeons Trauma Quality Program Participant Use File between 2017 and 2020 analyzing motorcycle Associated injuries and fatalities in adult patients with moderate and severe injury severity score in relation to helmet use. multivariable regressions were utilized and adjusted for potential confounders. A subset analysis was performed for patients presenting with Abbreviated Injury Scale head >=3 and all other body regions <=2.

Results: 43,225 patients met study criteria, of which 24,389 (56.4%) were helmet users and 18,836 (43.6%) were not. Helmet use was associated with a 35% reduction in the relative risk of expiring in the hospital due to motorcycle-related injuries (aOR 0.65; 95% CI [0.59-0.70]; p < 0.001) and a decreased Intensive care unit length of stay (ICU-LOS) by half a day (B = -0.50; 95% CI [-0.77, -0.24]; p < 0.001).

Conclusion: Motorcycle riders without a helmet had significantly greater odds of increased in-hospital mortality and longer stays in the ICU than those who used a helmet. The results of this nationwide study support the need for continued research exploring the significance of helmet use and interventions aimed at improving helmet usage among motorcyclists.

Level of evidence: Prognostic and epidemiological, level III.

(C) 2023

  1. Background

In the last two decades, the number of registered motorcycles within the United States (U.S.) has more than doubled from 4.3 to 8.8 million [1,2]. Despite the increasing popularity of motorcycle usage, it is a highly dangerous activity that may lead to extensive polytrauma and result in disabling or fatal injuries [3]. Consequently, motorcycle fatalities have also had a substantial increase in the last two decades, with approxi- mately 50% of states within the U.S. noting an upwards trend in

* Corresponding author at: Department of Surgery, Division of Trauma and Surgical Critical Care, Orlando Regional Medical Center, 82 W Underwood St., Orlando, FL 32806, USA.

E-mail address: [email protected] (A. Elkbuli).

motorcycle fatalities since 2015 [1,2]. More recent data in 2020 saw an increase of 11% in motorcycle fatalities compared to the previous year with a total of 5579 motorcyclists deaths, representing an urgent need for intervention on this rising Public health concern for motorcycle riders in the U.S.

An important intervention for motorcycle riders is helmet use, which is estimated to be 40% effective in preventing motorcyclist fatal- ities per the National Highway Traffic Safety Administration (NHTSA) [4]. One study suggests that helmets may reduce the risk of death by up to 50%, and reduce the risk of motorcycle-related injury by almost 70% [5]. It is postulated that helmets may reduce mortality by reducing the severity of head injuries, and by decreasing potential complications during the treatment period [6]. Additional evidence from a study of eleven U.S. states supported this idea by finding that motorcycle helmet

https://doi.org/10.1016/j.ajem.2023.04.018

0735-6757/(C) 2023

users experienced less head and brain injury following a motorcycle crash [7]. Despite this evidence, helmet use across the United States re- mains inconsistent, with 31% of all motorcycle riders reporting that they do not frequently use or never use a helmet [8]. Inconsistent helmet use is perpetuated by the lack of effective legislation concerning helmet use across the U.S., with only 18 states having universal helmet laws, 29 having laws only for specific riders, and 3 states not having any policies at all [9]. Further, the NHTSA found that 57% of motorcyclist fatalities in states without universal helmet laws occurred in riders without a hel- met, compared to only 9% in states with universal helmet laws [8]. To- gether, the rising popularity of motorcycles with a lack of appropriate helmet use demonstrates a need for further research and legislation on improved safety and prevention measures.

Prior research has analyzed trends of motorcyclists injuries, fatali- ties, and the protective effect of helmet use in Motorcycle collisions [7]. However, there remains a lack of recent literature that investigates the association between helmet use, demographics, specific injuries, and injury severity for motorcyclists in the United States. For this rea- son, continued analysis of characteristics associated with helmet use in motorcyclist collisions is an area of important research, especially with the increase in motorcycle usage over the last 20 years [1]. There- fore, we aim to provide a comprehensive analysis of current motorcycle injuries, injury types, fatalities, and the impact of helmet use on these factors in order to highlight areas requiring future intervention and research.

  1. Methods
    1. Study design and data sources

This is a retrospective cohort study utilizing the American College of Surgeons (ACS) Trauma Quality Program (TQP) Participant Use File (PUF) database to analyze motorcycle-associated injuries and fatalities in relation to helmet use between 2017 and 2020. The ACS-TQP PUF da- tabase was used to gather patient demographics and metrics pertaining to in-hospital mortality and intensive care unit length of stay (ICU-LOS). A subset analysis was performed on patients with isolated head injury, defined by an AIS score >= 3 for head with all other body regions AIS <= 2. ACS-TQP PUF was used due to its strengths in representing na- tional de-identified trauma data that undergoes a strict screening and data validation process [10]. A data dictionary is provided by the ACS- TQP PUF for a comprehensive explanation of the dataset with included variables [10].

    1. Inclusion and exclusion criteria

Motorcycle riders ages 18 and older with moderate and severe inju- ries (ISS >= 15) who experienced a blunt or penetrating injury or fatality were included for analysis. Cases were excluded if the patient was dead on arrival, had an ISS score <= 14, or lacked information pertaining to age, race, gender, or helmet status (Fig. 1).

    1. Outcomes of interest

The primary outcome assessed in this study was adjusted motorcycle-related in-hospital mortality rate. The secondary outcome was ICU-LOS in days.

    1. Study population and subgroup population

Patients meeting inclusion criteria were stratified by ISS, including moderate and severe injuries. To avoid cases with pedestrian data, mo- torcycle cases were selected by mechanism of injury. Patients were then divided into two cohort groups based on helmet use or no helmet use. A subset analysis was performed on patients who had a head AIS >= 3 with

Fig. 1. Displays the Selection process with appropriate inclusion and exclusion criteria. We evaluated adult motorcycle cases with moderate and severe ISS scores (ISS >= 15). Cases were excluded if they lacked pertinent information pertaining to age, race, gender, or missing helmet status.

all other body regions AIS <= 2 to eliminate confounding effects of other injuries in the setting of polytrauma.

    1. Statistical analysis

Descriptive statistics were performed on all variables within the dataset. Comparison between helmet and non-helmet users were ana- lyzed. For categorical data such as presenting injury characteristics, chi-squared analysis was utilized. Independent t-tests were used for continuous data such as emergency department (ED) vitals within the subset analysis. A multivariable binary logistic regression for in- hospital mortality was utilized with adjustment for age, gender, race, in- surance status, total EMS time, hospital teaching status, and hospital trauma level. Similarly, a linear regression was utilized for ICU-LOS with the same adjustment variables. Total EMS time included EMS re- sponse time, EMS scene time, and EMS transport time, corresponding with the time from EMS dispatch to arrival on scene, time EMS spent on the scene, and time from the scene to the hospital [11-13]. A p-value <0.05 was considered statistically significant. IBM SPSS Statis- tics (Armonk, NY) v28.0 was used. This study was conducted in compli- ance with ethical standards, was reviewed by our institutional review board, and was deemed exempt.

  1. Results
    1. Population characteristics and demographics

Over the study period (2017-2020), approximately 43,225 motorcy- cle cases met inclusion criteria, which were further stratified by helmet

use (n = 24,389, 56.4%) or no helmet use (n = 18,836, 43.6%) (Table 1). Helmet users were most commonly male (n = 22,055, 90.4%), white (n = 18,973, 77.8%), between the ages of 18-34 (n = 9238, 37.9%),

Table 2

Presenting injury characteristics for motorcycle-related injuries and fatalities in helmet and non-helmet users with available injury characteristic data. P-values were calculated from chi-squared analysis between helmet and non-helmet users.

and had private or Commercial insurance (n = 14,001, 58.4%)

Presenting Injuries

Helmet users

Non-helmet users

P-value

(n = 7515)

(n = 5842)

Chest Trauma

365 (1.5%)

155 (0.8%)

<0.001

>=2 extremity fractures

752 (3.1%)

496 (2.6%)

0.006

pelvic fracture

450 (1.8%)

210 (1.1%)

<0.001

Skull fracture

131 (0.5%)

395 (2.1%)

<0.001

Crushed extremity

449 (1.8%)

218 (1.2%)

<0.001

Extremity amputation

176 (0.7%)

80 (0.4%)

<0.001

Paralysis

222 (0.9%)

116 (0.6%)

<0.001

RR <10 or >29

941 (3.9%)

795 (4.2%)

0.057

SBP < 90

1023 (4.2%)

570 (3.0%)

<0.001

GCS <= 13

3006 (12.3%)

2807 (14.9%)

<0.001

(Table 1). Similarly, most non-helmet users were also male (n = 16,767, 89.0%), white (n = 15,236, 80.9%), between the ages of 18-34 (n = 5881, 31.2%), and with private or commercial insurance (n = 9642, 52.3%) (Table 1).

    1. Presenting injury characteristics

Motorcycle riders who used a helmet presented with a significantly lower rate of Skull fractures compared to non-helmet users (helmet n = 131, 0.5% vs. no helmet n = 395, 2.1%; p < 0.001). However, helmet users experienced a significantly greater number of bodily injuries, in- cluding chest trauma (helmet n = 365, 1.5% vs. no helmet n = 155, 0.8%; p < 0.001), pelvic fractures, (helmet n = 450, 1.8% vs. no helmet n = 210, 1.1%; p < 0.001) and greater than two extremity fractures (hel- met n = 752, 3.1% vs. no helmet n = 496, 2.6%; p < 0.006) (Table 2). Cases of a crushed extremity (helmet n = 449, 1.8% vs. no helmet n = 218, 1.2%; p < 0.001), extremity amputation (helmet n = 176, 0.7% vs. no helmet n = 80, 0.4%; p < 0.001), and paralysis (helmet n = 222, 0.9% vs. no helmet n = 116, 0.6%; p < 0.001) were also signif- icantly higher among helmet users (Table 2). Non-helmet users were more likely to present with a Glasgow Coma Scale score <= 13

RR = respiratory rate; SBP = systolic blood pressure; GCS = Glasgow Coma Scale.

(helmet n = 3006, 12.3% vs. no helmet n = 2807, 14.9%; p < 0.001) when compared to helmet users (Table 2).

    1. Mortality and ICU-LOS regression outcomes

Helmet use was associated with a 35% reduction in the relative risk of expiring in the hospital due to motorcycle-related injuries compared to non-helmet users (aOR 0.65; 95% CI [0.59-0.70]; p < 0.001) (Table 3).

Table 1

Demographic data of helmet and non-helmet users for all motorcycle-related injuries and fatalities. Age, sex, race, insurance status, hospital teaching status, and trauma center level display frequency (n) and percentage as well as p-values from chi-squared analysis. EMS transport time and ICU length of stay display mean with standard deviation and p-value from independent t-test.

Patient Demographics

Helmet users

Non-helmet users

P-value

(n = 24,389)

(n = 18,836)

Age

18-34

9238 (37.9%)

5881 (31.2%)

<0.001

35-50

6409 (26.3%)

5974 (31.7%)

0.071

51-69

7549 (31.0%)

6332 (33.6%)

<0.001

70-89

1193 (4.9%)

649 (3.4%)

0.810

Sex

Male

22,055 (90.4%)

16,767 (89.0%)

<0.001

Female

2329 (9.6%)

2065 (11.0%)

<0.001

Race

White

18,973 (77.8%)

15,236 (80.9%)

<0.001

Black

2644 (10.8%)

1768 (9.4%)

<0.001

Asian

318 (1.3%)

163 (0.9%)

<0.001

American Indian

98 (0.4%)

102 (0.5%)

0.034

Pacific Islander

69 (0.3%)

81 (0.4%)

0.010

Other

2287 (9.4%)

1486 (7.9%)

<0.001

Insurance Status private/commercial insurance

14,001 (58.4%)

9642 (52.3%)

0.618

Self-pay

3062 (12.8%)

3166 (17.2%)

0.684

Medicaid

3181 (13.3%)

2895 (15.7%)

0.777

Medicare

1816 (7.6%)

1417 (7.7%)

0.113

Hospital Teaching Status Academic

3334 (13.7%)

1515 (13.4%)

0.536

Community

8109 (33.2%)

6921 (36.7%)

0.371

Non Teaching

3518 (14.4%)

2684 (14.2%)

0.743

University

9189 (37.3%)

6578 (34.9%)

0.655

Trauma Center Level Level I

12,244 (65.3%)

9753 (67.4%)

0.053

Level II

5952 (31.8%)

4173 (28.8%)

0.084

Level III

542 (2.9%)

546 (3.8%)

0.232

EMS Transport Time (minutes) EMS Scene time

Mean (SD): 31.8 (13.9)

Mean (SD): 31.8 (14.9)

0.609

EMS Departure time

Mean (SD): 39 (11.8)

Mean (SD): 42 (12.8)

0.854

ICU Length of Stay (days)

ICU Length of Stay

Mean (Median): 8.0 (5.0)

Mean (Median): 8.4 (5.0)

<0.001

Table 3

multivariate regression analysis of motorcycle injuries and fatalities between 2017 and 2020 from ACS-TQIP PUF databases (n = 43,225). Regressions were adjusted for age, gender, race, insurance status, total EMS transport time, hospital teaching status, and hospital trauma level.

In Hospital Mortality

ICU Length of Stay

Helmet (%)

Non-Helmet (%)

Adjusted

95% confidence interval (OR)

Adjusted P-value

Adjusted

95% confidence interval (OR)

Adjusted P-value

Helmet

24,389 (56.4%)

18,836 (43.6%)

0.65 (0.59, 0.70)

<0.001

-0.50 (-0.77, -0.24)

<0.001

Constant

0.08

<0.001

6.80 (3.55, 10.66)

<0.001

Helmet use in our study was associated with a decreased ICU-LOS compared to non-helmet users by half of a day. (B = -0.50; 95% CI [-0.77, -0.24]; p < 0.001) (Table 3).

    1. AIS >= 3 for head, and all other body regions AIS <= 2

Adult motorcyclists who presented with isolated head injury as de- fined above were selected for further analysis. A total of 227 patients were included, stratified by helmet use (n = 138) and non-helmet use (n = 89). No significant difference was seen for in-hospital mortal- ity (helmet: n = 9, 7.0% vs. no helmet: n = 9, 12.2%; p = 0.211) or ICU- LOS (helmet: 9.12 days vs no helmet: 8.02 days; p = 0.485) (Table 4).

  1. Discussion

Our analysis demonstrated that patients with moderate to severe in- juries who used a helmet had significantly decreased odds of expiring in the hospital due to motorcycle-related injuries and had a significantly decreased ICU-LOS compared to non-helmet motorcycle riders. Injured motorcycle riders were most often white males with private or commercial insurance. Interestingly, helmet users were less likely to experience skull fractures, but were more likely to present with motorcycle-related bodily injuries than those without a helmet, includ- ing chest trauma, pelvic fractures, and greater than two extremity fractures. Helmet users were also more likely to have a crushing ex- tremity injury, extremity amputation, and paralysis. In our subset anal- ysis isolating motorcycle-related head injuries, there was no significant difference in our outcomes of in-hospital mortality or ICU-LOS based on helmet use.

Table 4 Data from a subset analysis of AIS Severity >=3 and remaining body regions with AIS <= 2. TBI pupillary response, in-hospital mortality, ICU length of stay, and vitals were not signifi- cantly different between helmet and non helmet users. Chi-squared analysis was used for categorical variables, pupillary response, and in-hospital mortality. Independent t-tests were used for continuous variables, ICU-LOS, and presenting vitals.

Variables Helmet users Non-helmet users P-value TBI Pupillary Response (%)

Both Reactive

40 (75.5%)

34 (63.0%)

0.161

One Reactive

1 (0.7%)

5 (9.3%)

0.097

Neither Reactive

12 (8.7%)

15 (27.8%)

0.541

In-Hospital Mortality (%)

In-hospital mortality

9 (7.0%)

9 (12.2%)

0.211

ICU-LOS (Mean)

ICU-LOS

9.12

8.02

0.485

Mean

Median

Mean

Median

Vitals

SBP

126.0

131.0

128.6

136.5

0.608

Pulse Rate

92.3

89.0

84.8

90.0

0.080

Temperature

36.7

36.7

36.5

36.6

0.118

Respiratory Rate

18.8

18.0

17.6

18.0

0.194

Pulse Oximetry

95.9

98.0

93.5

98.0

0.228

Height

175.9

177.8

171.9

177.4

0.127

Weight

89.8

84.0

89.6

85.5

0.959

ICU-LOS: Intensive Care Unit length of stay; SBP: Systolic Blood Pressure.

Mortality is a significant concern in motorcycle riders, as evidenced by increasing mortality rates despite established risk-reduction efforts with helmet use [2,8]. In our larger cohort, motorcycle riders who used a helmet had significantly decreased odds of expiring in the hospi- tal due to motorcycle-related injuries compared to riders without a hel- met. Choi et al. demonstrated similar findings of a reduction in in- hospital mortality when motorcycle riders used a helmet [14]. Though this is a well-documented finding, our analysis of a nationwide database further strengthens this evidence by controlling for other potentially confounding variables such as patient demographics, injury type, EMS transport time, and trauma center level.

Patients with motorcycle-related injuries and mortality who used a helmet spent significantly fewer days in the ICU compared to patients who did not use a helmet. This evidence is consistent with previous lit- erature, including data from Spencer et al. who demonstrated similar findings that motorcycle riders who used helmets had a significantly shorter ICU-LOS than patients without a helmet [15]. Medical costs in- curred after a motorcycle crash are notably higher in non-helmet users, as they are more likely to have increased ICU-LOS, hospital LOS, and ventilation days [16]. Thus, the economic impact caused by injuries in non-helmet users, as evidenced by longer ICU-LOS, is substantial, highlighting the need for improved helmet policies and legislation to better address this issue.

An analysis of injury characteristics in patients with motorcycle- related injuries and/or mortality in our study found that riders who used a helmet were more likely to present with bodily injuries, including chest trauma, pelvic fracture, paralysis, fracture of multiple extremities, or crushed or amputated extremities. This contrasts with non-helmet users, who presented more often with motorcycle-related skull fractures. Additionally, non-helmet users were more likely to have a GCS <= 13 on presentation. These findings are consistent with other studies, such as Khor et al. who found that non-helmeted riders had a higher incidence of head injuries and TBI when examining the im- pact of helmet usage on Cervical spine injuries [17]. Additionally, data from Lastfogel et al. demonstrated a similar phenomenon of increased bodily injuries among helmet users, attributing this increase to the pro- tective effects of helmets, postulating that helmeted riders are able to withstand higher forces in collisions, and therefore may ultimately end up with more extensive bodily injuries while maintaining protec- tion to the head [18]. Future studies may further evaluate the circum- stances surrounding increased bodily injury patterns in helmet users, such as investigating specific collision types, speed of collisions, vehicles involved, and roadway conditions which ACS-TQIP does not provide. Further research may help establish additional preventative safety mea- sures to reduce the risk of bodily injuries in helmet users.

As the incidence of polytrauma is high in patients presenting after motorcycle injury, our study utilized a subset analysis that isolated pa- tients with head injuries in order to minimize the effects of multiple other injuries on our results. We found that there was no significant difference between helmet and non-helmet users in relation to our primary outcome of in-hospital mortality or secondary outcome of ICU-LOS for this subset analysis. A possible explanation for this finding would be that cases selecting for AIS head severity >=3 as a proxy for se- vere TBI are already invariably more fatal regardless of helmet status. Emami et al. supported this notion with evidence that adults with head AIS >= 3 with bodily injury severity less than three (AIS <3) had a

mortality rate of nearly 85% when accompanied by a GCS of 3 [19]. An- other possible explanation is that the protective effect of helmets led to a reduced AIS compared to non-helmet users, and thus selecting for pa- tients with a head AIS >= 3 removed this protective benefit. Previous re- search found that non-helmet users were twice as likely to be found to have a TBI with head AIS > 2, supporting this theory [17].

This study has several limitations. While we assessed the impact of helmet use on motorcycle fatalities, we were unable to account for dif- ferences in helmet quality or type, therefore we are unable to conclude whether there is a significant difference between the helmets utilized. Furthermore, TQP-PUF does not provide information on injuries and fa- talities by state, therefore we were unable to evaluate if differences in helmet laws, population density, or other state specific factors play an important role in fatalities or rate of helmet use. Next, ACS-TQP-PUF ex- periences delay in information release, hindering our ability to assess more recent years. While a multivariable regression was performed to account for known predictors of mortality, it is possible that additional non-medical confounders were present that could not be corrected for using the ACS-TQP-PUF database, such as the fact that people who wear helmets to begin with may also be generally more “careful” drivers. Additionally, there is potential for an underestimation of the value of helmets by excluding dead on arrival patients, however many of these patients did not have the specific breakdown of head or bodily injury and were therefore excluded to minimize confounding. Finally, a subset analysis was performed to control for confounding effects of polytrauma, however this led to a small sample size that limited the sta- tistical evaluations we were able to perform. Despite these limitations, we believe this is an informative study that utilizes a national database to study the impact of helmet use in relation to motorcycle trauma. Mo- torcycle crashes account for thousands of injuries and fatalities each year and represent a significant Economic burden. Protective interven- tions such as helmet use need to be continually studied and emphasized to decrease the severity of injuries and mortality caused by motorcycle collisions.

We offer the following recommendations moving forward. The im- portance of Preventative measures such as helmet use cannot be overstated. Given that helmet use was associated with lower motorcycle-related in-hospital mortality rates in our study, we recom- mend policy makers and researchers continue to evaluate effective pol- icies and interventions aimed at improving helmet usage among motorcyclists. Additionally, we suggest the development and imple- mentation of educational materials, including evidence-based recom- mendations specific to helmet use and Mortality benefits, both during the licensing process and beyond. Next, we recommend that ACS-TQP- PUF consider including geographic information into the dataset to allow for future researchers to be able to consider this variable for future studies. Based on our findings of increased bodily injuries in helmet users, we suggest that further research is needed into additional protec- tive measures that may decrease this risk. Finally, we recommend that future research should investigate factors associated with motorcycle crashes, such as roadway conditions, speed of collisions, and vehicles involved, to better identify the cause of accidents and direct action towards improving those conditions.

  1. Conclusion

Our findings indicate that helmet use in adult motorcycle trauma pa- tients with moderate and severe injuries significantly decreases the odds of expiring in the hospital due to motorcycle-related injuries and significantly decreases ICU-LOS. Additionally, helmet users are more likely to present with motorcycle-related bodily injuries and disability such as chest trauma, pelvic fracture, paralysis, multiple extremity fractures, crushed extremities and/or amputation, in contrast to non- helmet users who are more likely to present with a skull fracture. Overall, the results of this nationwide study suggest that non-helmet users who are involved in Motorcycle accidents have worse outcomes,

including increased risk of in-hospital mortality and longer ICU-LOS, which may impose significant and lasting health and economic burdens. The findings of this study support the ongoing necessity of highlighting the significance of helmet use among motorcyclists and future research on motorcycle crash conditions to direct creation of improved legisla- tion and preventative strategies.

Author contribution

Study design and conception, project adminstration and supervi- sion: AE.

Investigation, data collection, curation, formal analysis, and interpre- tations: AE, AR, TB, MN.

Manuscript preparation- original draft: AR, TB, MN, NA, GB, PM, AE. Critical revision of the manuscript- review & editing: AR, TB, MN, NA,

GB, PM, LK, AE.

All authors read and approved the final manuscript.

Funding/financial disclosure

None.

CRediT authorship contribution statement

Abigail Rosander: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Data curation. Tessa Breeding: Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis. Micah Ngatuvai: Writing – review & editing, Writing – original draft, Investigation, Funding acquisition. Noah Alter: Writing – review & editing, Writing – original draft, Investigation. Piueti Maka: Writing – review & editing, Writing – original draft, Investigation. George Beeton: Writing – review & editing, Writing – original draft, Investigation. Lucy Kornblith: Writing – review & editing, Investigation. Adel Elkbuli: Writing – review & editing, Writing – original draft, Supervision, Resources, Project admin- istration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

Declaration of Competing Interest

Authors disclose no competing interest.

References

  1. Chaudhuri U, Ratnapradipa KL, Shen S, Rice TM, Smith GA, Zhu M. Trends and pat- terns in fatal US Motorcycle crashes, 2000-2016. Traffic Inj Prev. 2019;20(6):641-7.
  2. Ngatuvai M, Rosander A, Maka P, Beeton G, Fanfan D, Sen-Crowe B, Newsome K, Elkbuli A. Nationwide Analysis of Motorcycle-Associated Injuries and Fatalities in the United States: Insufficient Prevention Policies or Abandoned Laws? Am Surg. 2022 Jul;21:31348221117033. https://doi.org/10.1177/00031348221117033. Epub

ahead of print. PMID: 35861293.

  1. Race MC, Carlile MC. Motorcycle-related injuries: the high costs of riding. Tex Med. 2004;100(10):56-63.
  2. National Highway Traffic Safety Administration. Motorcycles (Traffic Safety Facts. Report No. DOT HS 813 112). Washington, DC: U.S. Department of Transportation; 2021. Accessed 25 September 2022. Available at: https://crashstats.nhtsa.dot.gov/ Api/Public/ViewPublication/813112externalicon.
  3. Liu BC, Ivers R, Norton R, Boufous S, Blows S, Lo SK. Helmets for preventing injury in motorcycle riders. Cochrane Database Syst Rev. 2008.(1) CD004333.
  4. Lee JL, Chen TC, Huang HC, Chen RJ. How motorcycle helmets affect trauma mortal- ity: clinical and policy implications. Traffic Inj Prev. 2017;18(6):666-71.
  5. Olsen CS, Thomas AM, Singleton M, et al. Motorcycle helmet effectiveness in reduc- ing head, face and brain injuries by state and helmet law. Inj Epidemiol. 2016;3(1):8.
  6. Motorcycle Helmet Use in 2020–Overall Results. TRAFFIC SAFETY FACTS Research Note. Published June. Accessed October 28, 2022. https://crashstats.nhtsa.dot.gov/ Api/Public/ViewPublication/813183; 2021.
  7. Motorcyclists. State Laws. Accessed October 27. https://www.ghsa.org/state-laws/ issues/Motorcyclists; 2022.
  8. Trauma Quality Programs Participant Use File. ACS. Accessed October 5. https:// www.facs.org/quality-programs/trauma/tqp/center-programs/ntdb/datasets; 2022.
  9. Satty T, Ramgopal S, Elmer J, Mosesso VN, Martin-Gill C. EMS responses and non- transports during the COVID-19 pandemic. Am J Emerg Med. 2021;42:1-8.
  10. Elkbuli A, Dowd B, Sanchez C, Shaikh S, Sutherland M, McKenney M. Emergency medical service transport time and trauma outcomes at an urban level 1 trauma center: evaluation of prehospital emergency medical service response. Am Surg. 2022;88(6):1090-6.
  11. Trauma Quality Programs Participant Use File. American College of Surgeons. Accessed October 30. https://www.facs.org/quality-programs/trauma/tqp/center- programs/ntdb/datasets; 2022.
  12. Choi WS, Cho JS, Jang YS, Lim YS, Yang HJ, Woo JH. Can helmet decrease mortality of craniocerebral trauma patients in a motorcycle accident?: a propensity score matching. PloS One. 2020;15(1):e0227691.
  13. Kuo SCH, Kuo PJ, Rau CS, Chen YC, Hsieh HY, Hsieh CH. The protective effect of hel- met use in motorcycle and bicycle accidents: a propensity score-matched study based on a trauma registry system. BMC Public Health. 2017;17(1):639.
  14. Heldt KA, Renner CH, Boarini DJ, Swegle JR. Costs associated with helmet use in mo- torcycle crashes: the cost of not wearing a helmet. Traffic Inj Prev. 2012;13(2): 144-9.
  15. Khor D, Inaba K, Aiolfi A, et al. The impact of helmet use on outcomes after a motor- cycle crash. Injury. 2017;48(5):1093-7.
  16. Lastfogel J, Soleimani T, Flores R, et al. Helmet use and injury patterns in motorcycle- related trauma. JAMA Surg. 2016;151(1):88-90.
  17. Emami P, Czorlich P, Fritzsche FS, et al. Impact of Glasgow coma scale score and pupil parameters on mortality rate and outcome in pediatric and adult severe traumatic brain injury: a retrospective, Multicenter cohort study. J Neurosurg. 2017;126(3): 760-7.

Leave a Reply

Your email address will not be published. Required fields are marked *