Injury Prevention最新文献

{"title":"New motivation for injury prevention in the face of dramatic demographic changes in China.","authors":"Wanbao Ye, Shuxian Yu, Zhaojing Yang, Liping Li","doi":"10.1136/ip-2024-045578","DOIUrl":"https://doi.org/10.1136/ip-2024-045578","url":null,"abstract":"<p><p>Our manuscript reviewed the enormous number of deaths caused by different types of injuries in China and around the world and proposed a new motivation for injury prevention to address sharp demographic changes and promote constant economic growth when China faces ageing, fewer children and industrial upgrading.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical and psychological factors related to injury, illness and tactical performance in law enforcement recruits: a systematic review.","authors":"Myles Murphy, Nicole Merrick, Gill Cowen, Vanessa Sutton, Garth Allen, Nicolas H Hart, Andrea B Mosler","doi":"10.1136/ip-2023-045150","DOIUrl":"10.1136/ip-2023-045150","url":null,"abstract":"<p><strong>Objective: </strong>There are inconsistent reports of factors relating to injury, illness and tactical performance in law enforcement recruits. Our objectives were to: (1) report physical and psychological risk factors and protective factors for injury and illness and (2) report physical and psychological risk factors and protective factors for tactical performance success.</p><p><strong>Design: </strong>Systematic epidemiological review.</p><p><strong>Methods: </strong>Searches of six databases were conducted on 13 December 2022. We included cohorts that assessed physical and psychological factors for injury, illness and tactical performance success. Study quality was assessed using the Joanna Briggs Institute Quality Assessment Checklist for Prevalence Studies and certainty assessed using the Grading of Recommendations Assessment, Development and Evaluation.</p><p><strong>Results: </strong>30 studies were included, and quality assessment was performed. Very low certainty of evidence exists for physical variables related to injury risk, and we found no studies that investigated psychological variables as a risk factor for injury. Low-certainty evidence found older age, poorer performance with push-up reps to failure, poorer arm ergometer revolutions, poorer beep test, poorer 75-yard pursuit and the 1.5 miles run tests to be associated with reduced tactical performance. Very low certainty of evidence exists that the psychological variables of intelligence and anger are associated with tactical performance.</p><p><strong>Conclusions: </strong>We identified a lack of high-level evidence for factors associated with injury, illness and performance. Interventions based on this research will be suboptimal. We suggest context-specific factors related to injury, illness and performance in law enforcement populations are used to inform current practice while further, high-quality research into risk factors is performed.</p><p><strong>Prospero registration number: </strong>CRD42022381973.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":"9-17"},"PeriodicalIF":2.5,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in alcohol, MDMA, methylamphetamine and THC in injured and deceased motor vehicle drivers and motorcyclists over a decade (2010-2019) in Victoria, Australia.","authors":"Jennifer Schumann, Matthew Di Rago, Noel Woodford, Linda Glowacki, John Fitzpatrick, Michael Kelly, Ben Beck, Olaf H Drummer, Dimitri Gerostamoulos, Joanna F Dipnall","doi":"10.1136/ip-2024-045342","DOIUrl":"https://doi.org/10.1136/ip-2024-045342","url":null,"abstract":"<p><strong>Background: </strong>Driving under the influence of alcohol and other drugs contributes significantly to road traffic crashes worldwide. This study explored trends of alcohol, methylamphetamine (MA), 3,4-methylenedioxy-N-methylamphetamine (MDMA) and Δ9-tetrahydrocannabinol (THC), in road crashes from 2010 to 2019 in Victoria, Australia.</p><p><strong>Methods: </strong>We conducted a cross-sectional analysis using data from the Victorian Institute of Forensic Medicine and Victoria Police, examining proscribed drug detections in road crashes. Time series graphs per substance explored indicative trends and comparisons between road users. Negative binomial regression models, with robust SEs and adjusted for exposure (kilometres travelled, Victorian licence holders), modelled the incidence rate ratio, with a Bonferroni-adjusted α=0.007 for multiple comparisons.</p><p><strong>Results: </strong>There were 19 843 injured drivers and 1596 fatally injured drivers. MA had the highest prevalence (12.3% of fatalities and 9.1% of injured drivers), demonstrating an increase over time. Overall, 16.8% of car drivers and motorcyclists tested positive for one or more drugs, with 14% of crashes involving a blood alcohol concentration (BAC)≥0.05%. MA and THC were the most common drugs in fatalities. Between 2015 and 2019, MA was detected in 27.9% of motorcyclist fatalities, followed by THC (18.3%) and alcohol ≥0.05% (14.2%), with similar but lower frequencies among injured motorcyclists. Alcohol detections (≥0.05% BAC) in fatalities declined, but increased in injured motorcyclists and car drivers until plateauing in 2017. THC detections rose among injured drivers until 2018, detected in 8.1% and 15.2% of injured and fatal drivers, respectively. MDMA-positive driving decreased among injured drivers and remained stable at ~1% of fatalities.</p><p><strong>Conclusions: </strong>Despite enhanced road safety measures in Victoria, drug-driving persists, indicating a need for revised prevention strategies targeting this growing issue.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting 90-day return to the emergency department in orthopaedic trauma patients in the Southeastern USA: a machine-learning approach.","authors":"Bruno Valan, Aaron Therien, Emily Peairs, Solomon Ayehu, Joshua Taylor, Daniel Zeng, Steven Olson, Rachel Reilly, Christian Pean, Malcolm DeBaun","doi":"10.1136/ip-2024-045358","DOIUrl":"https://doi.org/10.1136/ip-2024-045358","url":null,"abstract":"<p><strong>Introduction: </strong>Return-to-acute-care metrics, such as early emergency department (ED) visits, are key indicators of healthcare quality, with ED returns following surgery often considered avoidable and costly events. Proactively identifying patients at high risk of ED return can support quality improvement efforts, allowing interventions to target vulnerable patients. With its predictive capabilities, machine learning (ML) has shown potential in forecasting various clinical outcomes but remains underutilised in orthopaedic trauma. This study uses a random forest model to predict 90-day ED return in orthopaedic trauma patients, aiming to identify high-risk individuals and elucidate risk factors associated with returns. This study hypothesised that a highly accurate model could be developed to predict patients at high risk of ED return within 90 days of surgery.</p><p><strong>Purpose: </strong>To develop and validate an ML model that predicts 90-day ED returns after orthopaedic trauma surgery using input data readily available in the electronic health record.</p><p><strong>Methods: </strong>This is a retrospective model development and validation study. The study used data from a registry that includes information on all orthopaedic surgeries conducted at a level 1 academic medical centre. Patients who underwent orthopaedic trauma between 1 January 2017 and 1 March 2023 were identified using common procedural terminology code. The model used demographic, comorbid and perioperative variables. Return to the ED was captured as a binary outcome. Model performance was evaluated using the area under the receiver operator curve (AUROC).</p><p><strong>Results: </strong>A total of 12 069 patients met the inclusion criteria. Patients were predominantly female (53%) and white (70%), with a median age of 55. The 90-day ED return rate was 14% (table 1). The random forest model identified body mass index, distance from the patient's residence to the hospital, age, length of hospital stay and complexity of procedure (work relative value unit) as significant predictors of ED return, each accounting for greater than 10% of the total importance across all features in the model (table 2). Further, the model displayed strong discrimination of patients returning to the ED (AUROC=0.74) (figure 1).</p><p><strong>Conclusions: </strong>The random forest model demonstrated predictive discrimination of 90-day ED returns. Critical predictors such as patient distance from the hospital suggest considering geographical and socioeconomic factors in postdischarge care planning. Operational factors such as length of stay or complexity of the procedure also predicted return to the ED. The study lays the groundwork for future predictive models in clinical decision-making and healthcare resource utilisation.</p><p><strong>Level of evidence: </strong>Level III, retrospective model development and validation study.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal clustering of drowning mortality in Iran, from 2005 to 2022.","authors":"Samaneh Ziyaee, Fatemeh Shahbazi, Reza Hajmanouchehri, Seyed Saeed Hashemi Nazari","doi":"10.1136/ip-2024-045356","DOIUrl":"https://doi.org/10.1136/ip-2024-045356","url":null,"abstract":"<p><strong>Background: </strong>Drowning is a serious and neglected public health threat, and prevention of drowning has a multisectoral nature and requires multidimensional research. Therefore, this study aimed to evaluate the spatio-temporal variation in fatal unintentional drowning rates among the Iranian population from 2005 to 2022.</p><p><strong>Methods: </strong>In this repeated cross-sectional study, registry data were extracted from legal medicine organisations during 2005-2022. The mortality rate per 1 million population was calculated by gender and province. The joinpoint regression model was fitted to estimate average annual percentage changes and an annual percentage change in the drowning mortality rate. We used spatial scan statistics to detect high-risk clusters of drowning deaths at the provincial level.</p><p><strong>Results: </strong>Over 17 years 19 547 people died due to unintentional drowning. The highest yearly drowning rate was 15.58 per 1 000 000, and men had the highest rates of death (25.91) compared with women (4.98) in 2019. The overall mortality rate has decreased from 18.69 in 2005 to 12.87 in 2022. In the spatio-temporal analysis, four statistically significant high-risk clusters were detected in the north, southeast and centre of Iran.</p><p><strong>Conclusion: </strong>The overall mortality rate in 2022 decreased compared with the 17-year period. In the spatial analysis, several high-risk clusters were identified in different locations, which highlights the importance of targeted and more comprehensive interventions. It seems that the prevention of drowning requires the effective participation of all responsible organisations and risk reduction plans in the field of environmental and individual risk factors.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can a novel computer vision-based framework detect head-on-head impacts during a rugby league tackle?","authors":"Manish Mohan, Dan Weaving, Andrew J Gardner, Sharief Hendricks, Keith A Stokes, Gemma Phillips, Matt Cross, Cameron Owen, Ben Jones","doi":"10.1136/ip-2023-045129","DOIUrl":"https://doi.org/10.1136/ip-2023-045129","url":null,"abstract":"<p><strong>Background: </strong>Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby.</p><p><strong>Methods: </strong>This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts. Tackle events from professional rugby league matches were coded as either head-on-head or non-head-on-head impacts. These included non-televised standard-definition and televised high-definition video clips to train (n=341) and test (n=670) the framework. A computer vision framework consisting of two deep learning networks, an object detection algorithm and three-dimensional Convolutional Neural Networks, was employed and compared with the analyst-coded criterion. Sensitivity, specificity and positive predictive value were reported.</p><p><strong>Results: </strong>The overall performance evaluation of the framework to classify head-on-head impacts against manual coding had a sensitivity, specificity and positive predictive value (95% CIs) of 68% (58% to 78%), 84% (78% to 88%) and 0.61 (0.54 to 0.69) in standard-definition clips, and 65% (55% to 75%), 84% (79% to 89%) and 0.61 (0.53 to 0.68) in high-definition clips.</p><p><strong>Conclusion: </strong>The study introduces a novel computer vision framework for head-on-head impact detection. Governing bodies may also use the framework in real time, or for retrospective analysis of historical videos, to establish head-on-head rates and evaluate prevention strategies. Future work should explore the application of the framework to other head-contact mechanisms and also the utility in real time to identify potential events for clinical assessment.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Head and neck injuries from personal motorised vehicles in the United States.","authors":"Shaun Edalati, Sujay Ratna, Jacqueline Slobin, Nathan J Wallace, Satish Govindaraj, Alfred Marc Iloreta","doi":"10.1136/ip-2024-045453","DOIUrl":"https://doi.org/10.1136/ip-2024-045453","url":null,"abstract":"<p><strong>Background: </strong>The use of personal electric vehicles in the United States has increased head and neck injuries. This study analyses the types, frequencies, demographics and management of these injuries across motorised vehicles.</p><p><strong>Methods: </strong>This study uses 2020-2023 data from the National Electronic Injury Surveillance System to analyse injuries from various powered vehicles, incorporating diagnostic, event-related and demographic factors. In addition, the study evaluates non-powered skateboard-related injuries, which provided a comparable baseline for motorised vehicle injuries.</p><p><strong>Results: </strong>Our analysis included 3721 head and neck injuries: 1359 from scooters, 1743 from skateboards and 619 from hoverboards. Males sustained most injuries, accounting for 64% of scooter accidents, 74% of skateboard accidents and 58% of hoverboard accidents. Hospitalisation was required in 10% of scooter accidents, 9.2% of skateboard accidents and 6.9% of hoverboard accidents. Males, alcohol use and drug use were associated with increased risk of hospitalisation (p=0.00002, p=0.00004 and p<0.00001, respectively). Internal injury (37%) and lacerations (24%) were the most common types of injury. In cases, wear helmets were worn involving helmets, there were no significant differences in hospitalisation rates.</p><p><strong>Discussion: </strong>These findings underscore the need for improved injury prevention strategies, including more effective helmet designs and infrastructure enhancements, to reduce the growing burden of micromobility vehicle-related head and neck injuries.</p><p><strong>Conclusion: </strong>The rising incidence of head and neck injuries associated with personal mobility vehicles highlights the need for injury management and prevention strategies. Helmets may mitigate head and neck injuries associated with motorised scooters, skateboards and hoverboards.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explanatory capacity of measures of community context for paediatric injury hospitalisations in the USA.","authors":"Henry T Puls, Clemens Noelke, Kristyn N Jeffries, Daniel M Lindberg, Anna E Austin, Barbara H Chaiyachati, Matthew Hall","doi":"10.1136/ip-2024-045423","DOIUrl":"https://doi.org/10.1136/ip-2024-045423","url":null,"abstract":"<p><strong>Objective: </strong>Community context influences children's risk for injury. We aimed to measure the explanatory capacity of two ZIP code-level measures-the Child Opportunity Index V.3.0 (COI) and median household income (MHHI)-for rates of paediatric injury hospitalisations.</p><p><strong>Methods: </strong>This was a retrospective cross-sectional population-based study of children living in 19 US states in 2017. We examined injury hospitalisation rates for three categories: physical abuse among children <5 years, injuries suspicious for abuse among infants <12 months and unintentional injuries among children <18 years. Hospitalisation counts were obtained from the Healthcare Cost and Utilization Project and population data from the US Census. The COI is a multidimensional measure of communities' education, health and environment and social and economic characteristics. We used pseudo R² values from Poisson regression models to describe the per cent of variance in rates of each injury category explained by the COI and MHHI.</p><p><strong>Results: </strong>The COI explained 75.4% of the variability in rates of physical abuse, representing a 13.5% improvement over MHHI. The COI explained 58.5% of the variability in injuries suspicious for abuse, a 20.7% improvement over MHHI. The COI and MHHI explained 85.7% and 85.8% of the variability in unintentional injuries, respectively; results differed when unintentional injuries were stratified by mechanism and age.</p><p><strong>Implications: </strong>The COI had superior explanatory capacity for physical abuse and injuries suspicious for abuse compared with MHHI and was similar for unintentional injury hospitalisations. COI represents a means of accounting for community advantage in paediatric injury data, research and prevention.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143004863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthcare spending and factors associated with fall injury in Australia residential aged care: a cohort analysis.","authors":"Charles Okafor, Namal N Balasooriya, Amy T Page, Anne-Marie Hill, Christopher D Etherton-Beer, Tracy Comans","doi":"10.1136/ip-2024-045516","DOIUrl":"https://doi.org/10.1136/ip-2024-045516","url":null,"abstract":"<p><strong>Background: </strong>Given that fall injury is a critical public health concern in Australia, understanding the economic implications of falls among older adults is crucial to allocating healthcare resources efficiently to reduce falls and improve quality of life. This study therefore aimed to estimate the cost and identify factors associated with fall-related injuries within residential aged care (RAC).</p><p><strong>Methods: </strong>A cohort analysis from the healthcare system perspective based on data from a double-blinded randomised controlled trial-the Opti-Med trial. The trial participants were 303 people aged ≥65 years. Identification of in-scope data from the trial dataset was achieved using the falls description note and the National Hospital Cost Data Collection diagnostic related group classification system. Data analyses were performed using STATA V.17. All costs were adjusted to 2022 Australian dollars.</p><p><strong>Results: </strong>On average, the cost of an injurious fall per incident was $2494 (SD=$6199), while the average cost of falls per resident annum was $1798 (SD=$6002). The potential cost of injurious falls per annum in Australia's RAC system was $325 million. Sex and body mass index (BMI) were identified factors associated with fall injury. There was an inverted U-shaped relationship between BMI and falls risk in RAC.</p><p><strong>Conclusions: </strong>The healthcare spending on fall injury per resident annum in RAC represents 20% of the 2021-2022 healthcare expenditure per capita. The high cost and inverted U-shaped relationship between BMI and falls risk underscores the need for more effective and RAC-tailored falls prevention strategies in this setting.</p><p><strong>Trial registration numbers: </strong>Australian New Zealand Clinical Trial Registry (ACTRN12613001204730); WHO Universal Trial (U1111-1148-6094).</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142970551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alcohol-related injuries from e-scooter and e-bike use in the US (2019-2022): a retrospective study.","authors":"Edwin Akomaning, Samuel Prince Osei, Akshaya Srikanth Bhagavathula","doi":"10.1136/ip-2024-045461","DOIUrl":"10.1136/ip-2024-045461","url":null,"abstract":"<p><strong>Background: </strong>The use of electric-powered scooters and bikes (e-scooters/bikes) is rising, but little is known about associated injuries and substance use. This study analysed the trends and factors associated with e-scooter/bike-related injuries and alcohol/substance use emergency department (ED) visits from 2019 to 2022.</p><p><strong>Methods: </strong>A retrospective analysis of US ED visit data from the 2019-2022 National Electronic Injury Surveillance System (NEISS) identified visits for e-scooter/bike-related injuries. NEISS data were collected using stratified, multistage sampling, and the analysis accounted for this complex sampling design. Outcomes included yearly visits, patient demographics, injury details and alcohol/substance use associations. Multivariable logistic regression analysed factors associated with e-bike/scooter-related injury ED visits and alcohol/substance use.</p><p><strong>Results: </strong>Of 4020 e-scooter/bike injury ED visits, 3700 (weighted estimate 279 990) were e-scooters and 320 (weighted estimate 16 600) were e-bikes. Visits increased three-fold from 2019 (n=22 835) to 2022 (n=65 892). Most of the injuries involved males, with 79.6% of e-scooter injuries and 79.7% of e-bike injuries), aged 18-39 years (51.5% e-scooter, 48.5% e-bike) and non-Hispanic White (34.9% e-scooter, 38.8% e-bike). Alcohol use was reported in 8.6% of e-scooters and 2.5% of e-bike injury-related ED visits. Males had 2.6 times higher odds of alcohol use (OR: 2.61, 95% CI: 1.84 to 3.69) and 2.2 times higher odds of substance use (OR: 2.23, 95% CI: 1.19 to 4.16) associated ED visits, compared with females. Compared with the 18-39-year age group, those aged 10-17 years had 7.5 and 4.1 times higher odds of alcohol and substance use leading to e-scooter and e-bike injury-related ED visits, respectively.</p><p><strong>Conclusions: </strong>E-scooter injuries are increasing rapidly, especially among younger males, with a three-fold increase from 2019 to 2022. Alcohol and substance use both contribute significantly to morbidity. Strengthening policy and prevention approaches like the use of helmets are warranted to improve e-scooter/bike safety.</p>","PeriodicalId":13682,"journal":{"name":"Injury Prevention","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142800663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}