A generalized ordered logit analysis of risk factors associated with driver injury severity

2019 ◽  
Author(s):  
Eric Nimako Aidoo ◽  
Williams Ackaah
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Logit Analysis ◽  
Ordered Logit ◽  
Factors Associated ◽  
Generalized Ordered Logit

Risk Factors Associated With Pressure Ulcers in Patients With Traumatic Brain Injury Admitted to the Intensive Care Unit

2021 ◽  
pp. 105477382110504
Author(s):  
Jeong Eun Yoon ◽  
Ok-Hee Cho
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Traumatic Brain Injury ◽  
Intensive Care ◽  
Brain Injury ◽  
Medical Records ◽  
Injury Severity ◽  
University Hospital ◽  
Factors Associated ◽  
Braden Scale

Pressure injuries (PIs) are one of the most important and frequent complications in patients admitted to the intensive care unit (ICU) or those with traumatic brain injury (TBI). The purpose of this study was to determine the incidence and risk factors of PIs in patients with TBI admitted to the ICU. In this retrospective study, the medical records of 237 patients with TBI admitted to the trauma ICU of a university hospital were examined. Demographic, trauma-related, and treatment-related characteristics of all the patients were evaluated from their records. The incidence of PIs was 13.9%, while the main risk factors were a higher injury severity score, use of mechanical ventilation, vasopressor infusion, lower Braden Scale score, fever, and period of enteral feeding. This study advances the nursing practice in the ICU by predicting the development of PIs and their characteristics in patients with TBI.

scholarly journals Crash risk factors associated with injury severity of teen drivers

2019 ◽  
Vol 43 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Venkata R. Duddu ◽  
Venu Madhav Kukkapalli ◽  
Srinivas S. Pulugurtha
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Crash Risk ◽  
Factors Associated ◽  
Teen Drivers

scholarly journals Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach

2020 ◽  
Vol 17 (11) ◽  
pp. 4111
Author(s):  
Ali J. Ghandour ◽  
Huda Hammoud ◽  
Samar Al-Hajj
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Injury Severity ◽  
Road Traffic ◽  
Road Traffic Injury ◽  
Outcome Variable ◽  
Contributing Factors ◽  
Road Crashes ◽  
Factors Associated ◽  
Ensemble Machine Learning

Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.

scholarly journals Risk Factors Affecting Driver Severity of Single- Vehicle Run Off Road Crash for Thailand Highway

2020 ◽  
Vol 24 (5) ◽  
pp. 207-216
Author(s):  
Chamroeun Se ◽  
Thanapong Champahom ◽  
Sajjakaj Jomnonkwao ◽  
Vatanavongs Ratanavaraha
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Information Management System ◽  
Logit Analysis ◽  
Factors Affecting ◽  
Roadside Safety ◽  
Fatal Crash ◽  
Run Off ◽  
Single Vehicle ◽  
Safety Features

Single-Vehicle Run Off Road (ROR) crash has been the leading crash type in terms of frequency and severity in Thailand. In this study, multinomial logit analysis was applied to identify the risk factors potentially influencing driver injury severity of single-vehicle ROR crash using accident records between 2011 and 2017 which were extracted from Highway Accident Information Management System (HAIMS) database. The analysis results show that the age of driver older than 55 years old, male driver, driver under influence of alcohol, drowsiness, ROR to left/right on straight roadway increase the probability of fatal crash, while other factors are found to mitigate severity such as the age of driver between 26-35 years old, using seatbelt, ROR and hit fixed object on straight and curve segment of roadway, mounted traffic island, intersection-related and accident in April. This study recommends the need to improve road safety campaign, law enforcement, and roadside safety features that potentially reduce level of severity of driver involving in single-vehicle ROR crash.

Homicidal Physical Violence: An Exploratory Analysis of Relevant Cognitive, Demographic, and Criminological Factors in a Sample of Murderers

2020 ◽  
pp. 108876792093516
Author(s):  
Callie Mazurek ◽  
Michael Brook ◽  
Mary Kwasny ◽  
Robert E. Hanlon
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Physical Violence ◽  
Preliminary Evidence ◽  
Exploratory Analysis ◽  
Physical Injury ◽  
Factors Associated ◽  
Violent Offending ◽  
Offender Characteristics ◽  
Neuropsychological Evaluations

Homicide injury severity (HIS), the degree of physical injury inflicted on a victim during a homicide, has emerged as a relevant criminological variable. However, little is known regarding the offender characteristics and criminological variables that may be associated with greater HIS. Data (demographic, cognitive, and criminological variables) from forensic neuropsychological evaluations of N = 101 offenders convicted of murder, were explored in relation to the Homicide Injury Scale. Numerous factors were related to HIS. Results partially replicate prior findings of factors associated with violent offending and provide preliminary evidence for distinct risk factors for inflicting severe injury during a homicide.

scholarly journals Understand e-bicyclist safety in China: Crash severity modeling using a generalized ordered logit model

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878162 ◽  
Author(s):  
Tao Wang ◽  
Jun Chen ◽  
Chen Wang ◽  
Xiaofei Ye
Keyword(s):  
Traffic Control ◽  
Logit Model ◽  
Injury Severity ◽  
Age Groups ◽  
Industrial Area ◽  
Ordered Logit ◽  
The Road ◽  
Ordered Logit Model ◽  
Generalized Ordered Logit ◽  
Generalized Ordered Logit Model

A thorough crash modeling effort was made to examine e-bicyclists’ injury severity, using a generalized ordered logit model, which can capture the ordinal nature of injury severity and allow for heterogeneity across observations. A number of factors associated with injury severity of e-bicyclists are identified, including older e-bicyclists, heavy truck involved, e-bicyclist at fault, e-bicyclist turn left, e-bicyclist cross the road, driver turn right, industrial area, weekday, and tree separation. Safety countermeasures and interventions are thus proposed based on the modeling results, including developing educational programs for specific age groups (e.g. older e-bicyclists, female e-bicyclists, and inexperienced drivers), launching safety campaigns, improving geometric design and traffic control in low-developed area, curve roads, and signalized intersections. Moreover, some interesting research topics are also suggested, such as examining head-on e-bike crash mechanism, crash mechanism between e-bicyclists and heavy trucks and motorcycles, and safety effects of different separation treatments on e-bicyclists’ injury severity from kinetic and kinematic perspectives.

scholarly journals Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

2019 ◽  
Vol 11 (19) ◽  
pp. 5194 ◽  
Author(s):  
Natalia Casado-Sanz ◽  
Begoña Guirao ◽  
Antonio Lara Galera ◽  
Maria Attard
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Weather Conditions ◽  
Crash Data ◽  
Factors Associated ◽  
The Road ◽  
Pedestrian Injury ◽  
On The Road ◽  
Pedestrian Crashes

According to the Spanish General Traffic Accident Directorate, in 2017 a total of 351 pedestrians were killed, and 14,322 pedestrians were injured in motor vehicle crashes in Spain. However, very few studies have been conducted in order to analyse the main factors that contribute to pedestrian injury severity. This study analyses the accidents that involve a single vehicle and a single pedestrian on Spanish crosstown roads from 2006 to 2016 (1535 crashes). The factors that explain these accidents include infractions committed by the pedestrian and the driver, crash profiles, and infrastructure characteristics. As a preliminary tool for the segmentation of 1535 pedestrian crashes, a k-means cluster analysis was applied. In addition, multinomial logit (MNL) models were used for analysing crash data, where possible outcomes were fatalities and severe and minor injured pedestrians. According to the results of these models, the risk factors associated with pedestrian injury severity are as follows: visibility restricted by weather conditions or glare, infractions committed by the pedestrian (such as not using crossings, crossing unlawfully, or walking on the road), infractions committed by the driver (such as distracted driving and not respecting a light or a crossing), and finally, speed infractions committed by drivers (such as inadequate speed). This study proposes the specific safety countermeasures that in turn will improve overall road safety in this particular type of road.

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