机动车辆故意造成的大规模伤亡事故中受伤和死亡人数的相关因素:扩大应急响应的及时援助。

IF 2.1 4区 医学 Q2 EMERGENCY MEDICINE Prehospital and Disaster Medicine Pub Date : 2024-02-01 Epub Date: 2024-01-11 DOI:10.1017/S1049023X23006726
Eva Maria Valiño, Rafael Castro-Delgado, Silvia Sola Muñoz, Barry Lynam, Pedro Castro
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引用次数: 0

摘要

导言:在西方国家,将机动车辆(MV)作为武器的蓄意大规模伤亡事件(IMCIs)呈增长趋势。这种方法导致每起事件的伤亡率在蓄意大规模伤亡事件中最高。因此,迫切需要及时、准确地估算机动车引发的儿童意外伤害事故的伤亡情况,以调整必要的医疗资源:研究目的:本研究旨在确定与 MV-IMCI 初始阶段伤亡人数相关的因素:这是一项回顾性、观察性、分析性研究,研究对象是 2000-2021 年间世界各地的中压-IMCIs。数据来自三个不同来源:有针对性的汽车撞击大规模伤亡袭击(TARMAC)袭击数据库、全球恐怖主义数据库(GTD)以及维基百科网站上的汽车撞击袭击页面。雅各布斯公式用于估算车辆行驶路线上的人口密度。主要结果变量是伤亡(受伤和死亡)总人数。使用斯皮尔曼相关系数和简单线性回归分析变量之间的关联:46起机动车交通事故造成了1636人伤亡(1430人受伤,206人死亡),其中大部分是由汽车造成的。最常见的驾驶模式是在接近目标时加速,平均时速在 4 至 130 公里之间,行驶距离在 10 至 2 260 米之间。在 MV-IMCI 场景中估计的人数在 36-245717 人之间。受影响人数与现场估计人数呈明显正相关(R2:0.64;95% CI,0.61-0.67;P 2:0.42;95% CI,0.40-0.44;P = .004):结论:受影响区域的估计人数和车辆的平均速度是与机动车交通事故中伤亡人数相关的最重要变量,有助于及时估计伤亡人数。
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Factors Associated with the Number of Injured and Fatalities in Motor Vehicle Intentional Mass-Casualty Incidents: A Timely Aid for Scaling the Emergency Response.

Introduction: Intentional mass-casualty incidents (IMCIs) involving motor vehicles (MVs) as weapons represent a growing trend in Western countries. This method has resulted in the highest casualty rates per incident within the field of IMCIs. Consequently, there is an urgent requirement for a timely and accurate casualty estimation in MV-induced IMCIs to scale and adjust the necessary health care resources.

Study objective: The objective of this study is to identify the factors associated with the number of casualties during the initial phase of MV-IMCIs.

Methods: This is a retrospective, observational, analytical study on MV-IMCIs world-wide, from 2000-2021. Data were obtained from three different sources: Targeted Automobile Ramming Mass-Casualty Attacks (TARMAC) Attack Database, Global Terrorism Database (GTD), and the vehicle-ramming attack page from the Wikipedia website. Jacobs' formula was used to estimate the population density in the vehicle's route. The primary outcome variables were the total number of casualties (injured and fatalities). Associations between variables were analyzed using Spearman's correlation coefficient and simple linear regression.

Results: Forty-six MV-IMCIs resulted in 1,636 casualties (1,430 injured and 206 fatalities), most of them caused by cars. The most frequent driving pattern was accelerating whilst approaching the target, with an average speed range between four to 130km/h and a distance traveled between ten to 2,260 meters. The people estimated in the MV-IMCI scenes ranged from 36-245,717. A significant positive association was found of the number affected with the estimated crowd in the scene (R2: 0.64; 95% CI, 0.61-0.67; P <.001) and the average vehicle speed (R2: 0.42; 95% CI, 0.40-0.44; P = .004).

Conclusion: The estimated number of people in the affected area and vehicle's average speed are the most significant variables associated with the number of casualties in MV-IMCIs, helping to enable a timely estimation of the casualties.

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来源期刊
Prehospital and Disaster Medicine
Prehospital and Disaster Medicine Medicine-Emergency Medicine
CiteScore
3.10
自引率
13.60%
发文量
279
期刊介绍: Prehospital and Disaster Medicine (PDM) is an official publication of the World Association for Disaster and Emergency Medicine. Currently in its 25th volume, Prehospital and Disaster Medicine is one of the leading scientific journals focusing on prehospital and disaster health. It is the only peer-reviewed international journal in its field, published bi-monthly, providing a readable, usable worldwide source of research and analysis. PDM is currently distributed in more than 55 countries. Its readership includes physicians, professors, EMTs and paramedics, nurses, emergency managers, disaster planners, hospital administrators, sociologists, and psychologists.
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