Injuries associated with electric-powered bikes and scooters: analysis of US consumer product data

DiMaggio, Charles; Bukur, Marko; Wall, Stephen P.; Frangos, Spiros G.; Wen, Ai

doi:10.1136/injuryprev-2019-043418

Cited by 62 publications

(74 citation statements)

References 14 publications

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Section: Discussionmentioning

confidence: 99%

“…DiMaggio et al indicated that while there is some nuance and complexity to the injury picture for these vehicles, there is cause for concern 2 . Injury patterns for E‐bike and P‐scooters differ from each other and from traditional pedal operated bicycles in ways that will demand increased attention from transportation and public health officials 2 . The increased proportion of concussions reported by DiMaggio et al 2 as well as the high frequency of oral and maxillofacial injuries seen in this current report may be due, at least in part, to the lack of use of helmets and other protective measures by E‐bike and P‐scooter users.…”

Section: Discussionmentioning

confidence: 99%

“…Injury patterns for E‐bike and P‐scooters differ from each other and from traditional pedal operated bicycles in ways that will demand increased attention from transportation and public health officials 2 . The increased proportion of concussions reported by DiMaggio et al 2 as well as the high frequency of oral and maxillofacial injuries seen in this current report may be due, at least in part, to the lack of use of helmets and other protective measures by E‐bike and P‐scooter users. While legal requirements for the use of protective measures when operating E‐bikes and P‐scooters is of great importance, any intervention should also include promotion, enforcement and evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…Electric‐Powered Bikes (E‐bikes) and powered scooters (P‐scooters) present a new method of transportation and are becoming commonly used worldwide 1,2 . The increasing popularity of electric scooters and E‐bikes has raised concerns about the potential for injury to riders and pedestrians 2 …”

Section: Introductionmentioning

confidence: 99%

“…In an analysis of injury patterns and trends associated with E‐bikes, P‐scooters, and pedal bicycles from 2000 to 2017 using the US National Electronic Injury Surveillance System it was found that E‐bike and P‐scooter use and injury patterns differ from more traditional pedal operated bicycles 2 . In Israel, there was a 13‐fold increase in the number of hospitalized patients due to E‐bike and P‐scooter incidents from 2013 to 2019 3 .…”

Section: Introductionmentioning

confidence: 99%

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Dental and maxillofacial injuries associated with electric‐powered bikes and scooters in Israel: A report for 2014‐2019

Lin

Goldman²,

Peleg³

et al. 2020

Dental Traumatology

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Background/Aims Electric‐Powered Bikes and powered scooters present a new method of transportation and are becoming commonly used worldwide. However, the reports on traumatic dental injuries related to their use are scarce. The aim of this study was to report the frequency and severity of dental and maxillofacial injuries associated with electric‐powered bikes and scooters in Israel between the years 2014 and 2019. Methods This was a retrospective cohort study based on data from the Israeli National Trauma Registry (INTR). The INTR provides comprehensive data on hospitalized patients from all six Level I trauma centers (TC) and 15 of the 20 Level II TCs in Israel. All injured patients who were hospitalized due to a traffic collision between 2014 and 2019 were identified. The data for those hospitalized due to an e‐bike or motorized scooter accident were extracted as well as for pedestrians who were injured as a result of a crash with these vehicles. Results A total of 3,686 hospital admissions were related to electric‐powered bikes and scooters. Of those, 378 (10.3%) were oral and maxillofacial injuries. Most of the oral and maxillofacial injuries were attributed to powered bikes (321 out of 378; 84.92%) and the rest to powered scooters. There was a constant increase in general as well as the oral and maxillofacial injuries during the study years. Almost 20% of the cases involved injuries to the teeth. Overall, 291 pedestrians were reported to be injured due to electric‐powered bikes and scooters; 29 (9.97%) of them, suffered from oral and maxillofacial injuries. Most of those were children aged 0‐15 years (41.38%) and elders older than 60 years (37.39%). Conclusions Trauma related to electric‐powered bikes and scooters is an increasing concern. Dental professionals should be actively involved in educational and legislative efforts focusing on the prevention of e‐bike and scooter‐related injuries, in general, and specifically maxillofacial injuries.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dental and maxillofacial injuries associated with electric‐powered bikes and scooters in Israel: A report for 2014‐2019

Lin

Goldman²,

Peleg³

et al. 2020

Dental Traumatology

View full text Add to dashboard Cite

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Injuries With Electric vs Conventional Scooters and Bicycles

Fernandez,

Li,

Patel

et al. 2024

JAMA Netw Open

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ImportanceMicromobility, the use of small vehicles (primarily scooters and bicycles), has become a standard transportation method in the US. Despite broad adoption of electric micromobility vehicles, there is a paucity of data regarding the injury profiles of these vehicles, particularly in the US.ObjectiveTo characterize micromobility injury trends in the US, identify demographic characteristic differences in users of electric and conventional vehicles, and identify factors associated with hospitalization.Design, Setting, and ParticipantsThis cross-sectional study queried the National Electronic Injury Surveillance System, a comprehensive database that collates injury data associated with consumer products from emergency departments across the US to provide national estimates, from calendar year 2017 to 2022. Data on micromobility vehicle injuries (bicycles, scooters, electric bicycles [e-bicycles], and electric scooters [e-scooters]) were obtained.Main Outcomes and MeasuresTrends in injury and hospitalization counts, injury characteristics, and factors associated with hospitalization.ResultsFrom 2017 to 2022, the US recorded 2 499 843 bicycle (95% CI, 1 948 539-3 051 147), 304 783 scooter (95% CI, 232 466-377 099), 45 586 e-bicycle (95% CI, 17 684-73 488), and 189 517 e-scooter (95% CI, 126 101-252 932) injuries. The median age of the riders was 28 (IQR, 12-51) years; 72% were male, 1.5% Asian, 13% Black, 12% Hispanic, and 49% White. Annual e-bicycle and e-scooter injuries increased from 751 (95% CI, 0-1586) to 23 493 (95% CI, 11 043-35 944) and injuries increased from 8566 (95% CI, 5522-11 611) to 56 847 (95% CI, 39 673-74 022). Compared with conventional vehicles, electric vehicle accidents involved older individuals (median age, 31 vs 27 years; P &lt; .001) and a higher proportion of Black riders (25% vs 12%; P &lt; .001). Helmet use was less in electric vehicle incidents compared with conventional vehicles (43% vs 52%; P = .02), and injuries were more common in urban settings (83% vs 71%; P = .008). Age-adjusted odds of hospitalization among all Black individuals compared with White individuals was 0.76 (95% CI, 0.59-0.98; P = .04).Conclusions and RelevanceIn this cross-sectional study of micromobility vehicles, an increased number of injuries and hospitalizations was observed with electric vehicles compared with conventional vehicles from 2017 to 2022. These findings suggest the need for change in educational policies, infrastructure, and law to recenter on safety with the use of micromobility vehicles.

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Use of Generative AI to Identify Helmet Status Among Patients With Micromobility-Related Injuries From Unstructured Clinical Notes

Burford,

Itzkowitz,

Ortega

et al. 2024

JAMA Netw Open

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ImportanceLarge language models (LLMs) have potential to increase the efficiency of information extraction from unstructured clinical notes in electronic medical records.ObjectiveTo assess the utility and reliability of an LLM, ChatGPT-4 (OpenAI), to analyze clinical narratives and identify helmet use status of patients injured in micromobility-related accidents.Design, Setting, and ParticipantsThis cross-sectional study used publicly available, deidentified 2019 to 2022 data from the US Consumer Product Safety Commission’s National Electronic Injury Surveillance System, a nationally representative stratified probability sample of 96 hospitals in the US. Unweighted estimates of e-bike, bicycle, hoverboard, and powered scooter–related injuries that resulted in an emergency department visit were used. Statistical analysis was performed from November 2023 to April 2024.Main Outcomes and MeasuresPatient helmet status (wearing vs not wearing vs unknown) was extracted from clinical narratives using (1) a text string search using researcher-generated text strings and (2) the LLM by prompting the system with low-, intermediate-, and high-detail prompts. The level of agreement between the 2 approaches across all 3 prompts was analyzed using Cohen κ test statistics. Fleiss κ was calculated to measure the test-retest reliability of the high-detail prompt across 5 new chat sessions and days. Performance statistics were calculated by comparing results from the high-detail prompt to classifications of helmet status generated by researchers reading the clinical notes (ie, a criterion standard review).ResultsAmong 54 569 clinical notes, moderate (Cohen κ = 0.74 [95% CI, 0.73-0.75) and weak (Cohen κ = 0.53 [95% CI, 0.52-0.54]) agreement were found between the text string–search approach and the LLM for the low- and intermediate-detail prompts, respectively. The high-detail prompt had almost perfect agreement (κ = 1.00 [95% CI, 1.00-1.00]) but required the greatest amount of time to complete. The LLM did not perfectly replicate its analyses across new sessions and days (Fleiss κ = 0.91 across 5 trials; P &lt; .001). The LLM often hallucinated and was consistent in replicating its hallucinations. It also showed high validity compared with the criterion standard (n = 400; κ = 0.98 [95% CI, 0.96-1.00]).Conclusions and RelevanceThis study’s findings suggest that although there are efficiency gains for using the LLM to extract information from clinical notes, the inadequate reliability compared with a text string–search approach, hallucinations, and inconsistent performance significantly hinder the potential of the currently available LLM.

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Injuries associated with electric-powered bikes and scooters: analysis of US consumer product data

Cited by 62 publications

References 14 publications

Dental and maxillofacial injuries associated with electric‐powered bikes and scooters in Israel: A report for 2014‐2019

Dental and maxillofacial injuries associated with electric‐powered bikes and scooters in Israel: A report for 2014‐2019

Injuries With Electric vs Conventional Scooters and Bicycles

Use of Generative AI to Identify Helmet Status Among Patients With Micromobility-Related Injuries From Unstructured Clinical Notes

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