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Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention.

ABSTRACT: Road traffic injuries are a leading cause of morbidity and mortality globally. Understanding circumstances leading to road traffic injury is crucial to improve road safety, and implement countermeasures to reduce the incidence and severity of road trauma. We aimed to characterise crash characteristics of road traffic collisions in Victoria, Australia, and to examine the relationship between crash characteristics and fault attribution. Data were extracted from the Victorian State Trauma Registry for motor vehicle drivers, motorcyclists, pedal cyclists and pedestrians with a no-fault compensation claim, aged > = 16 years and injured 2010-2016. People with intentional injury, serious head injury, no compensation claim/missing injury event description or who died < = 12-months post-injury were excluded, resulting in a sample of 2,486. Text mining of the injury event using QDA Miner and Wordstat was used to classify crash circumstances for each road user group. Crashes in which no other was at fault included circumstances involving lost control or avoiding a hazard, mechanical failure or medical conditions. Collisions in which another was predominantly at fault occurred at intersections with another vehicle entering from an adjacent direction, and head-on collisions. Crashes with higher prevalence of unknown fault included multi-vehicle collisions, pedal cyclists injured in rear-end collisions, and pedestrians hit while crossing the road or navigating slow traffic areas. We discuss several methods to promote road safety and to reduce the incidence and severity of road traffic injuries. Our recommendations take into consideration the incidence and impact of road trauma for different types of road users, and include engineering and infrastructure controls through to interventions targeting or accommodating human behaviour.

SUBMITTER: Giummarra MJ

PROVIDER: S-EPMC7840051 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention.

Giummarra Melita J MJ Beck Ben B Gabbe Belinda J BJ

PloS one 20210127 1

Road traffic injuries are a leading cause of morbidity and mortality globally. Understanding circumstances leading to road traffic injury is crucial to improve road safety, and implement countermeasures to reduce the incidence and severity of road trauma. We aimed to characterise crash characteristics of road traffic collisions in Victoria, Australia, and to examine the relationship between crash characteristics and fault attribution. Data were extracted from the Victorian State Trauma Registry ...[more]

PMID: 33503030

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Project description:BackgroundSince 2002, France has been strengthening legislation on road traffic. This study is intended to evaluate the changes in Traumatic Brain Injury (TBI) incidence and mortality resulting from Road Traffic Collision (RTC) in the two 6-year periods before and after 2002.MethodsWe used a Registry of all RTC casualties in the Rhône Department of France. Each casualty was coded according to the Abbreviated Injury Scale (AIS). The study describes changes in demographic variables, TBI (AIS ? 2) incidence and mortality, other body lesions (AIS ? 3) associated with TBI, road user types, seatbelt and helmet wearing.FindingsRTC casualty occurrences decreased by 21% (from 64,312 to 50,746) during the period after 2002. TBI occurrence accounted for 8.6% and 6.7% of all RTC in both periods. This corresponds to a reduction of TBI casualty incidence (-42%), which was much more pronounced than RTC casualty incidence (-25%) (p < 0.0001). Severe and critical TBI (AIS-4 and -5) incidences were reduced by half as much (-21%), compared to TBI incidence. TBI mortality rate (among population) and lethality (among TBI related to RTC casualties) decreased 56% and 23%, respectively. This reduction particularly affected car occupants and victims who deceased. TBI incidence decreased 43% in all 10-year age classes until 60 on average, this decrease declining with age in the period after 2002. After adjustment for age, sex, road user types, and severity of lesions at the head and other body regions, logistic regression analysis displayed a protective effect of the period following 2002, on the risk of death after RTC-related TBI.InterpretationThe greater reductions in the incidence, severity and mortality of TBI when compared with the reduction of casualty incidence have mainly affected car users. These results should be attributable to the improvements in standards of care, primary safety of the car fleet and general road architecture safety. However, the increased reduction in the TBI epidemics in France, when compared to those observed in other developed countries for the same periods, suggests that the effects should be strongly attributable to changes in road user behaviour induced by law enforcement. The at-risk groups for TBI after RTC are now two-wheel users (motorized or not) and individuals over 60 years of age.

Project description:BackgroundRoad traffic injuries (RTIs) are a growing but neglected global health crisis, requiring effective prevention to promote sustainable safety. Low- and middle-income countries (LMICs) share a disproportionately high burden with 90% of the world's road traffic deaths, and where RTIs are escalating due to rapid urbanization and motorization. Although several studies have assessed the effectiveness of a specific intervention, no systematic reviews have been conducted summarizing the effectiveness of RTI prevention initiatives specifically performed in LMIC settings; this study will help fill this gap.MethodsIn accordance with PRISMA guidelines we searched the electronic databases MEDLINE, EMBASE, Scopus, Web of Science, TRID, Lilacs, Scielo and Global Health. Articles were eligible if they considered RTI prevention in LMICs by evaluating a prevention-related intervention with outcome measures of crash, RTI, or death. In addition, a reference and citation analysis was conducted as well as a data quality assessment. A qualitative metasummary approach was used for data analysis and effect sizes were calculated to quantify the magnitude of emerging themes.ResultsOf the 8560 articles from the literature search, 18 articles from 11 LMICs fit the eligibility and inclusion criteria. Of these studies, four were from Sub-Saharan Africa, ten from Latin America and the Caribbean, one from the Middle East, and three from Asia. Half of the studies focused specifically on legislation, while the others focused on speed control measures, educational interventions, enforcement, road improvement, community programs, or a multifaceted intervention.ConclusionLegislation was the most common intervention evaluated with the best outcomes when combined with strong enforcement initiatives or as part of a multifaceted approach. Because speed control is crucial to crash and injury prevention, road improvement interventions in LMIC settings should carefully consider how the impact of improvements will affect speed and traffic flow. Further road traffic injury prevention interventions should be performed in LMICs with patient-centered outcomes in order to guide injury prevention in these complex settings.

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Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention.

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Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention.

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