Socioeconomic status and risk of car crash injury, independent of place of residence and driving exposure: results from the DRIVE Study

Chen, Huei Yang; Ivers, Rebecca; Martiniuk, Alexandra; Boufous, Soufiane; Senserrick, Teresa; Woodward, Mark; Stevenson, Mark; Norton, Robyn

doi:10.1136/jech.2009.091496

Cited by 59 publications

(36 citation statements)

References 40 publications

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“…Lastly, members of households with income of $50,000 or less (indicated by the LOWINCOME variable) are estimated to have 70 percent higher crash rates, as passengers, than those in households making over $50,000 a year. This result is consistent with findings by Chen et al (41), who concluded that drivers from lower socioeconomic areas face higher crash rates, as compared to drivers from higher status areas, after controlling for driving exposure and population density.…”

Section: Passenger Crash Propensitysupporting

confidence: 92%

Americans’ Crash Histories and Opinions on Safety Policy

Chen

Kockelman

2013

Transportation Research Record: Journal of the Transportation R

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This study examines the impact of driving experience and habits, citation histories, vehicle ownership, and demographics of over 1,000 Americans on their crash risk and safety policy opinions. Model results suggest that increased driver crash probability and decreased support for crash countermeasures correlate with criminal history and high tolerance for risk, both of which are more prevalent among men than women. Non-injury crash involvement (after controlling years of driving, as exposure metrics) appears more common for those with busy lifestyles: those with higher education and more household vehicles. Support for crash safety policies appears largely independent of a person's (and his/her family's) crash history, but linked to gender, marital status, and education levels. Women and lower income households are more likely to support safety policies, while those with higher educational obtainment are generally less likely. Finally, results suggest that unfamiliarity may be a key (but surmountable) barrier to introduction technology-based safety policies like speed limiters.).

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Section: Passenger Crash Propensitysupporting

confidence: 92%

Americans’ Crash Histories and Opinions on Safety Policy

Chen

Kockelman

2013

Transportation Research Record: Journal of the Transportation R

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“…Younger drivers are likely to be inexperienced in negotiating curves (Chen et al, 2010) as they are still novice drivers and coupled with poorer hazard perception skills, increased sensation-seeking propensity, and susceptibility to negative peer influence and risky decision making are at greater risk of crashing on road curves (Scott-Parker et al, 2009). Drivers may also under-or overestimate the sharpness of the curve and fail to safely negotiate the road curve.…”

Section: Contributing Factorsmentioning

confidence: 96%

A Novel Approach to Assessing Road-Curve Crash Severity

Rakotonirainy

Chen

Scott‐Parker

et al. 2014

Journal of Transportation Safety & Security

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Curves are a common feature of road infrastructure; however, crashes on road curves are associated with increased risk of injury and fatality to vehicle occupants. Countermeasures require the identification of contributing factors. However, current approaches to identifying contributors use traditional statistical methods and have not used selfreported narrative claim to identify factors related to the driver, vehicle, and environment in a systemic way. Text mining of 3,434 road-curve crash claim records filed between 1 January 2003 and 31 December 2005 at a major insurer in Queensland, Australia, was undertaken to identify risk levels and contributing factors. Rough set analysis was used on insurance claim narratives to identify significant contributing factors to crashes and their associated severity. New contributing factors unique to curve crashes were identified (e.g., tree, phone, oversteer) in addition to those previously identified via traditional statistical analysis of police and licensing authority records. Text mining is a novel methodology to improve knowledge related to risk and contributing factors to road-curve crash severity. Future road-curve crash countermeasures should more fully consider the interrelationships between environment, the road, the driver and the vehicle, and education campaigns in particular could highlight the increased risk of crash on road curves.

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“…The study sample's demographics and distribution of hypothesized risk factors for crash by level of psychological distress are presented in Table 1. Of the total sample, 5,822 (29%) reported low or no psychological distress (Kessler 10 score, 10-15), 7,664 (38 %) reported a moderate amount of distress (Kessler 10 score, 16-21), 4,992 (25%) reported high distress (Kessler 10 score, [22][23][24][25][26][27][28][29], and 1,535 (8%) reported very high distress (Kessler 10 score, 30-50). More females reported higher levels of distress than males.…”

Section: Resultsmentioning

confidence: 99%

“…The baseline survey asked participants about their psychological distress as well as other factors previously demonstrated to be risk factors for crash. Information on demographic factors was collected: age, sex, Australian Standard Geographic Classification for remoteness [22], and the Australian Bureau of Statistics Index for socio-economic status [23]. Information on exposure to driving and driver training were adapted from the Western Australian Young Driver Cohort Study [24] and the University of Otago Young Driver Pilot Study [25].…”

Section: Methodsmentioning

confidence: 99%