Trends in lawyer use in road traffic injury compensation claims.
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ABSTRACT:
SUBMITTER: Scollay CE
PROVIDER: S-EPMC7135282 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Trends in lawyer use in road traffic injury compensation claims.
Scollay Clare E CE Berecki-Gisolf Janneke J Grant Genevieve M GM
PloS one 20200406 4
Injury compensation claimants use legal services to help them navigate compensation schemes, including accessing benefits and resolving disputes. Little is known, however, about the extent of lawyer use by compensation claimants, including changes over time. This paper presents findings from one of the largest empirical investigations of lawyer use in an injury compensation setting to date. Using evidence from more than 275,000 claims in the road traffic injury scheme in the state of Victoria, A ...[more]
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Project description: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.
| S-EPMC7840051 | biostudies-literature
Project description:BackgroundRegaining good health and returning to work are important for people injured in road traffic crashes and for society. The handling of claims by insurance companies may play an important role in the rate at which health recovers and return to work is actually attained.MethodsA novel approach towards claims handling for people injured in road traffic accidents was compared to the standard approach. The setting was a large insurance company (NRMA Insurance) in the state of New South Wales, Australia. The new approach involved communicating effectively with injured people, early intervention, screening for adverse prognostic factors and focusing on early return to work and usual activities. Demographic and injury data, health outcomes, return to work and usual activities were collected at baseline and 7 months post-injury.ResultsSignificant differences were found 7 months post-injury on 'caseness' of depression (p = 0.04), perceived health limitation on activities (p = 0.03), and self-reported return to usual activities (p = 0.01) with the intervention group scoring better. Baseline general health was a significant predictor for general health at 7 months (OR 11.6, 95% CI 2.7-49.4) and for return to usual activities (OR 4.6, 95% CI 2.3-9.3).ConclusionWe found a few positive effects on health from a new claims handling method by a large insurance company. It may be most effective to target people who report low general health and low expectations for their health recovery when they file their claim.
| S-EPMC3292930 | biostudies-literature
Project description:ObjectivesEvery day, an average of 3,400 deaths and tens of millions of injuries occur as a result of traffic accidents. This study aims to model and validate road traffic injury (RTI) times series, specifically considering gender.Study designTime trend studies of monthly road traffic injuries (RTI) in Iran from March 2005 to February 2016, as well as those of males and females from March 2009 to February 2016 were performed.MethodsThe seasonal auto-regressive integrated moving average method (SARIMA) was employed to predict RTI time series. The final model was selected from various SARIMA models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). To examine whether the residuals were white noise, the Ljung-Box (LB) test and residuals plots were used for un-correlation, and the zero mean and stationarity, respectively. Additionally, smoothing methods were utilized to validate the SARIMA models for fitting and out-of-range prediction of the time series models under study. The sample auto-correlation function (ACF) and the partial autocorrelation function (PACF) with 20 lags were employed to determine the order of models and to ascertain if the residuals of the model were uncorrelated.ResultsBased on the obtained results, SARIMA (2,1,0)(0,1,1)12, SARIMA (0,1,1)(0,1,1)12, SARIMA (1,1,1)(0,0,1)12, and SARIMA (2,0,0)(1,0,0)12 were chosen for the time series including incidence rates of total road traffic injuries (IRTI), IRTI of males, females, and males-to-females, respectively. The AIC values were -87.57, 413.38, -732.91, and -85.32, respectively. The LB test for the residuals of the time series models of (0.539) IRTI, (0.3) IRTI of males, (0.23) females, and (0.237) males-to-females indicated that residuals were uncorrelated. Furthermore, prediction values for the next 24 months (2016 to 2018) showed no decline in the incidence rate of male and female traffic injuries. Results of the predictions using exponential smoothing methods indicated out-of-range prediction validity of the SARIMA models.ConclusionThis study exemplified the high efficiency of SARIMA models in predicting road traffic injuries (RTIs). Based on observations, the IRTI mean in Iran was 35.57 in 2016. The predicted values of the IRTI for 2016-2018 by the SARIMA model showed no decreasing trend. During the studied period, the observed values of IRTI for males were two to three times the female values. Thus, prediction of RTI can provide a useful tool for traffic safety policymaking by simulating interrupted time series when applying new traffic enforcement interventions and regulations in the future. Additionally, IRTI analysis of males and females showed that men had a non-increasing trend but higher incidence of traffic injuries, whereas the IRTI for women revealed an increasing trend from 2009 to 2012 with a lower incidence of injuries. This growth could be attributed to the impact of increased outdoor activities of women and the increased number of issued driving licenses in the period of 2009-2012.
| S-EPMC6508924 | biostudies-literature
Project description:INTRODUCTION:In this study we developed the Disability Beliefs Scale to assess Veterans' beliefs that engaging in treatment, as well as other behaviors, would affect the likelihood of a Veteran's being awarded disability-related benefits. We posited that Veterans with stronger beliefs that attending mental health treatment would facilitate a service-connection award would be more likely to attend PTSD treatment before their compensation and pension examinations for PTSD. METHODS:Electronic health records for 307 post-9/11-era Veterans applying for compensation and pension for service-connected PTSD and engaging in a clinical trial of a treatment-referral intervention were analyzed for PTSD-specific and more general mental health treatment use around the time of their compensation examinations. All participants completed the Disability Beliefs Scale and other baseline assessments. Multilevel models assessed change in treatment use as a function of time relative to the C&P exam, compensation examination status (before or after), and the interaction between examination status and beliefs about treatment benefits. RESULTS:No main effects of time or examination status were observed. As hypothesized, beliefs about treatment benefits moderated the effect of examination status on PTSD treatment use. Veterans believing more strongly that mental health treatment would help a claim differentially attended PTSD treatment before the examination than after. The effect was not observed for general mental health treatment use. CONCLUSION:The association between Veterans' use of PTSD treatment and their service-connection examination status was moderated by beliefs that receiving treatment affects the service-connection decision. This suggests that factors reported to motivate seeking service-connection-finances, validation of Veterans' experiences, and the involvement of significant others-might also help motivate Veterans' use of effective PTSD treatments. However, the results reflect correlations that could be explained in other ways, and service-connection was one of many factors impacting PTSD treatment engagement.
| S-EPMC6307722 | biostudies-literature
Project description:This study describes road traffic injuries among school-aged children in Guangzhou, China, and examines the effect of road safety knowledge and risk behaviours on road traffic injuries.A stratified cluster sample of 3747 children from six primary schools and six middle schools in Guangzhou, China, was surveyed. Data were collected on sociodemographic factors and road traffic injuries during the past year. Knowledge about road safety rules was assessed using a 14-item road safety knowledge index, and risky road safety behaviours were measured using a 25-item road safety behaviour index.A total of 403 (10.8%) students reported having at least one road traffic injury during the past 12 months. A high proportion of injuries was found among children who were boys, in primary school and from the suburbs. Bicycle-related injuries were the most common (46.0% of all injuries). Motor vehicle-related injuries had higher hospitalisation rates and worse psychological impact than bicycle or pedestrian injuries. Children with low and medium road safety knowledge had 1.5 to 3 times the odds of injury compared with students with high road safety knowledge. Students with high scores on the risky road behaviour index had twice the odds of injury (OR 2.04, 95% CI 1.47 to 2.84) compared with students with low scores.Better road safety knowledge and the avoidance of walking or cycling-related risk behaviours are protective factors for road traffic injuries among Chinese school children. More injury prevention programmes are needed to improve road safety knowledge and reduce risk behaviours.
| S-EPMC5507585 | biostudies-other
Project description:Road traffic collisions are a major cause of traumatic brain injury. However, the relationship between road traffic collision dynamics and traumatic brain injury risk for different road users is unknown. We investigated 2065 collisions from Great Britain's Road Accident In-depth Studies collision database involving 5374 subjects (2013-20). Five hundred and ninety-five subjects sustained a traumatic brain injury (20.2% of 2940 casualties), including 315 moderate-severe and 133 mild-probable injuries. Key pathologies included skull fracture (179, 31.9%), subarachnoid haemorrhage (171, 30.5%), focal brain injury (168, 29.9%) and subdural haematoma (96, 17.1%). These results were extended nationally using >1 000 000 police-reported collision casualties. Extrapolating from the in-depth data we estimate that there are ∼20 000 traumatic brain injury casualties (∼5000 moderate-severe) annually on Great Britain's roads, accounting for severity differences. Detailed collision investigation allows vehicle collision dynamics to be understood and the change in velocity (known as delta-V) to be estimated for a subset of in-depth collision data. Higher delta-V increased the risk of moderate-severe brain injury for all road users. The four key pathologies were not observed below 8 km/h delta-V for pedestrians/cyclists and 19 km/h delta-V for car occupants (higher delta-V threshold for focal injury in both groups). Traumatic brain injury risk depended on road user type, delta-V and impact direction. Accounting for delta-V, pedestrians/cyclists had a 6-times higher likelihood of moderate-severe brain injury than car occupants. Wearing a cycle helmet during a collision was protective against overall and mild-to-moderate-to-severe brain injury, particularly skull fracture and subdural haematoma. Cycle helmet protection was not due to travel or impact speed differences between helmeted and non-helmeted cyclist groups. We additionally examined the influence of the delta-V direction. Car occupants exposed to a higher lateral delta-V component had a greater prevalence of moderate-severe brain injury, particularly subarachnoid haemorrhage. Multivariate logistic regression models created using total delta-V value and whether lateral delta-V was dominant had the best prediction capabilities (area under the receiver operator curve as high as 0.95). Collision notification systems are routinely fitted in new cars. These record delta-V and automatically alert emergency services to a collision in real-time. These risk relationships could, therefore, inform how routinely fitted automatic collision notification systems alert the emergency services to collisions with a high brain injury risk. Early notification of high-risk scenarios would enable quicker activation of the highest level of emergency service response. Identifying those that require neurosurgical care and ensuring they are transported directly to a centre with neuro-specialist provisions could improve patient outcomes.
| S-EPMC8914876 | biostudies-literature
Project description:BackgroundRoad traffic injury (RTI) is a significant yet poorly characterized cause of morbidity and mortality in the Middle East. This hospital-based-study examined RTI in Lebanon and provided an understanding of their characteristics.MethodsWe collected prospective RTI data from three participating hospitals over 3 months using a designed tool based on Canadian CHIRPP and WHO tools. We performed logistic regression analysis to examine the relationship between contributing risk factors (age, sex) and injury types as well as the association of safety measures used (seatbelts or helmets) and body parts injured.ResultsA total of 153 patients were collected. Male preponderance with 72%, with mean age 32.6 (SD = 14.9) years. RTI was highest among passengers aged 15 to 29 (48%). Motorcyclists comprised the greatest injury proportion (38%), followed by vehicle-occupants (35%), and pedestrians (25%) (P = .04). Hip injuries represented the most affected body part (48.7%), followed by head/neck (38.2%). Only 31% (n = 47) of victims applied safety measures (seatbelts or helmets). Six drivers (7%) reported cell phone use at collision. The use of safety measures was associated with a substantial reduction in head/neck injuries (P = .03), spine injuries (P = .049), and lower risk of traumatic brain injury (TBI) (P = .02).ConclusionsRTI is a major health problem in Lebanon. Safety measures, though poorly adhered to, were associated with less severe injuries, and should be further promoted via awareness campaigns and enforcement. Trauma registries are needed to assess the RTI burden and inform safety interventions and quality-of-care improvement programs.
| S-EPMC8611407 | biostudies-literature
Project description:PurposeThe impact of road traffic crashes on health is well developed, in terms of deaths and direct consequences, but it is less so in terms of long-term life consequences. Few studies have compared the general impact on Health Related Quality of Life (HRQoL) following road traffic injury (RTI) by using a variety of different injured body parts and severity levels of the injury and compared them with a sample of non-injured referent individuals. Consequently, the aim of the current study is to assess how injury severity is associated with HRQoL, and if it differs between men, women, over age and injured body parts.MethodsThis cross-sectional study identified people with a RTI in the Swedish Traffic Accident Data Acquisition System (STRADA). A frequency matched reference group was also included. Data include both register data and self-reported HRQoL data.ResultsA total of 1788 out of 4761 persons with an RTI (37.6%) and 2186 out of 4761 reference persons (45.9%) returned the questionnaire, giving a total response rate of 41.9% (n = 3974). The findings show different patterns of HRQoL loss, depending on sex, age, injured body part, and levels of injury severity.ConclusionThe results show that even relatively minor road traffic injuries can lead to a significantly lower of HRQoL, especially for women, compared to the non-injured reference group. Moreover, when the inherent reduction of HRQoL over age was considered, the results indicated that younger individuals have a larger difference from the reference group in HRQoL, independent of the injury severity, compared to the older individuals; hence, an improved understanding of age and gender differences in HRQoL following an RTI is needed to better understand the long-term consequences of injuries from a public health perspective.
| S-EPMC7253518 | biostudies-literature
Project description:BackgroundRoad traffic accidents are one of the serious disasters that cause public injury, fatality and great economic loss. They are a growing public health problem around the world.ObjectivesThe aim of this study was to determine epidemiological characteristics, tendency and possible influencing factors of road traffic injuries (RTIs) in China, so as to give target suggestions on preventative measures.MethodsRoad traffic accident data were obtained from National Bureau of Statistics of China and Ministry of Transport of the People's Republic of China. Descriptive statistic such as RTIs frequency, trends of different accident types from 2007 to 2016; the RTIs difference between different regions and road surfaces were compared; and the possible influencing factors of RTIs were also explored.ResultsOver the past decade, with the mileage of constructed highway increased, the frequency of road traffic accidents have declined substantially in China, and the death toll from road traffic accidents with motor vehicles has declined from 2007 to 2015, Conversely, the number of deaths from non-motor vehicle accidents has risen rapidly since 2012. Our study showed that the traffic accident related mortality in Guizhou province was different from the level of the whole nation, and the Eastern, Central and Western areas of China were all significantly different (P < 0.001). Linear regression suggested a significant affected of gross domestic product (GDP)-per-capita, education level, the number of health institutions, populations, and car ownership status on traffic accident death tolls (P < 0.001). Moreover, cement concrete pavement roads were associated with the highest occurrence rates of RTI, and RTIs was statistically significant (P < 0.001) on different road surfaces.ConclusionEven though the frequency of road traffic accidents has declined, RTIs remain an urgent public health problem in China. Thus, the government should give some target preventative measures to reduce RTIs, aiming at different regions, the increasing trend of the death toll related to non-motor vehicles and the highest occurrence on cement concrete pavement roads.
| S-EPMC6688591 | biostudies-literature
Project description:Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strategies by legal limits. Using 4794 RTI drivers from secondary cross-sectional data from the Thai Governmental Road Safety Evaluation project in 2002-2004, the machine-learning models (Gradient Boosting Classifier: GBC, Multi-Layers Perceptrons: MLP, Random Forest: RF, K-Nearest Neighbor: KNN) and a parsimonious logistic regression (Logit) were developed for predicting the mortality risk from road-traffic injury in drunk drivers. The predictors included alcohol concentration level in blood or breath, driver characteristics and environmental factors. Of 4974 drivers in the derived dataset, 4365 (92%) were surviving drivers and 429 (8%) were dead drivers. The class imbalance was rebalanced by the Synthetic Minority Oversampling Technique (SMOTE) into a 1:1 ratio. All models obtained good-to-excellent discrimination performance. The AUC of GBC, RF, KNN, MLP, and Logit models were 0.95 (95% CI 0.90 to 1.00), 0.92 (95% CI 0.87 to 0.97), 0.86 (95% CI 0.83 to 0.89), 0.83 (95% CI 0.78 to 0.88), and 0.81 (95% CI 0.75 to 0.87), respectively. MLP and GBC also had a good model calibration, visualized by the calibration plot. Our machine-learning models can predict road-traffic mortality risk with good model discrimination and calibration. External validation using current data is recommended for future implementation.
| S-EPMC8508427 | biostudies-literature