Accident; analysis and prevention
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This study examined the correlates of injury severity using police records of pedestrian-motor-vehicle collisions on state routes and city streets in King County, Washington. Levels of influence on collision outcome considered (1) the characteristics of individual pedestrians and drivers and their actions; (2) the road environment; and (3) the neighborhood environment. Binary logistic regressions served to estimate the risk of a pedestrian being severely injured or dying versus suffering minor or no injury. ⋯ Road intersection design was significant only in the state route models, with pedestrians crossing at intersections without signals increasing the risk of being injured or dying. Adjusting for pedestrians' and drivers' characteristics and actions, neighborhood medium home values and higher residential densities increased the risk of injury or death. No other road or neighborhood environment variable remained significant, suggesting that pedestrians were not safer in areas with high pedestrian activity.
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This study identified contributing factors in the occurrence of motor vehicle crashes (MVCs) and the severity of crashes according to work-related status in Utah. Analyses were based on probabilistically linked data involving police crash reports and hospital inpatient and emergency department (ED) records for the years 1999-2005. Of 643,647 drivers involved in crashes, 73,437 (11.4%) went to the emergency department (ED) and 4989 (0.8%) were hospitalized. ⋯ Of those attending the ED because of a crash, workers were significantly more likely to have broken bones, bleeding wounds, or to die. Of those hospitalized because of a crash, workers were significantly less likely to have caused the crash (65% [145/223] vs. 73% [3,315/4,566], P<0.001). Yet although those drivers who were working at the time of the crash compared with those not working were less likely to have alcohol involved or to have caused the crash, there remains room for improvement among workers with respect to these factors, as well as safety belt use and fatigue.
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Road crashes not only claim lives and inflict injuries but also create an economic burden to the society due to loss of productivity. Although numerous studies have been conducted to examine a multitude of factors contributing to the frequency and severity of crashes, very few studies have examined the influence of street pattern at a community level. This study examined the effect of different street patterns on crash severity using the City of Calgary as a case study. ⋯ Their effects on injury risk are examined together with other factors including road features, drivers' characteristics, crash characteristics, environmental conditions and vehicle attributes. Pedestrian and bicycle crash data for the years 2003-2005 were utilized to develop a multinomial logit model of crash severity. Our results showed that compared to other street patterns, loops and lollipops design increases the probability of an injury but reduces the probability of fatality and property-damage-only in an event of a crash.
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Comparative Study
Prevalence rates of helmet use among motorcycle riders in a developed region in China.
This study aimed to determine the prevalence rates of helmet use, and of correct helmet use (chinstrap firmly fastened) among motorcycle riders and their passengers in Zhongshan, Guangdong Province, China. A cross-sectional survey involving direct observation of motorcycle riders was conducted at 20 randomly selected intersections. A total of 13,410 motorcycles were observed during a 10-day period in February 2009. ⋯ The helmet wearing rate on city streets was almost 95%, however city riders were more likely than rural riders to wear non-motorcycle helmets while riding. In multivariate analyses, factors associated with increased helmet use included riding on city streets, male gender, being a driver, carrying less passengers and riding a registered motorcycle. The results indicated enforcement and education activities need to be strengthened with respect to both helmet use and helmet quality, especially in rural areas, in order to improve wearing rates.
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A multivariate logistic regression model, based upon National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data for calendar years 1999-2008, was developed to predict the probability that a crash-involved vehicle will contain one or more occupants with serious or incapacitating injuries. These vehicles were defined as containing at least one occupant coded with an Injury Severity Score (ISS) of greater than or equal to 15, in planar, non-rollover crash events involving Model Year 2000 and newer cars, light trucks, and vans. The target injury outcome measure was developed by the Centers for Disease Control and Prevention (CDC)-led National Expert Panel on Field Triage in their recent revision of the Field Triage Decision Scheme (American College of Surgeons, 2006). ⋯ The area under the receiver operator characteristic (ROC) curve for the final model was 0.84. Delta-V (mph), seat belt use and crash direction were the most important predictors of serious injury. Due to the complexity of factors associated with rollover-related injuries, a separate screening algorithm is needed to model injuries associated with this crash mode.