A method for estimating delta-V distributions from injury outcomes in crashes

Two key national crash data samples from the National Automotive Sampling System (NASS) program are the General Estimates System (GES) and the Crashworthiness Data System (CDS). The former is a larger (50,000 crashes per year) sample of police-reported crashes and contains only information coded from the police report. The latter is a smaller (~3,000 crashes per year) sample of light-vehicle towaway crashes that are investigated by trained accident investigators. One key advantage of CDS is that it includes estimated crash severity, or delta-V, assigned to each vehicle in a crash. Delta-V is the best single predictor of injury outcome and thus is a key variable in prediction models. However, because it requires special data collection, it is absent from police-report-based datasets. In contrast to CDS, GES is a much larger sample and includes more than just light-vehicle crashes. Thus, it would be ideal to have delta-V available for GES crashes to improve models of injury outcome from those data. Some attempts to do this for individual crashes were unsuccessful (e.g., Farmer, 2003). However, in some analyses, especially statistical simulations, it is sufficient to have a distribution of delta-V for a given crash mode rather than tying a specific delta-V to a specific crash-involved vehicle. This report presents a method of estimating a distribution of crash severity using only police-reported crash data. The approach uses an injury risk curve developed from CDS and a parametric distributional assumption for the delta-V distribution. While the distribution can take any parametric form, I use the lognormal in this report. The method uses maximum likelihood to fit parameters to the delta-V distribution based on the observed injury distribution using the police-reported KABCO scale. That is, individuals in crashes fall into one of five injury categories. Each pair of lognormal parameters produces a distribution of injury when multiplied by the injury risk curve. Thus, the parameters that produce the multinomial injury distribution that best fits the observed distribution are chosen for the estimated delta-V distribution. The report includes results from a simulation study as well as a fit to CDS data with known delta-V distribution.National Highway Traffic Safety Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/117575/1/103241.pdfDescription of 103241.pdf : Final repor

Analysis of motorcycle crashes in Michigan 2009-2013

The goal of this analysis is to assess the consequences of the modification to the motorcycle helmet law that took effect on April 13, 2012, based on crash data from 2009-2013. The key areas of interest include: 1) changes in fatality and injury rates due to helmet non-use; 2) helmet use rates among crash-involved riders, especially those under 21; 3) out-of-state ridership, as it is seen in the crash data; 4) risk-taking behavior such as alcohol use and recklessness, as it relates to injury and fatality outcomes; and 5) motorcycle endorsements among crash-involved ridersMichigan State Police Office of Highway Safety Planninghttp://deepblue.lib.umich.edu/bitstream/2027.42/109726/1/103142.pd

Injury patterns in motor-vehicle crashes in the United States: 1998 - 2014

The NASS-CDS database for years 1998-2014 was analyzed to examine trends in injury patterns. To account for changes in data collection for years 2009 and later, most analyses focused on occupants in vehicles newer than 10 years relative to the given crash year. However, for analysis of trends by crash year, the number of occupants injured in older vehicle was estimated. The number of occupants with AIS2+ or AIS3+ injuries was assessed by main crash type (rollover, frontal, rear, near-side, and far-side) and AIS body region (head, face, neck, thorax, spine, abdomen, upper extremity and lower extremity). Risk of AIS2+ or AIS3+ injury was also calculated. Dependent variables include occupant age, BMI, gender, occupant seating position, and restraint; vehicle type and model year; plus crash year. Additional analyses were performed to determine if injury patterns varied within body region. Overall trends in injury indicate a substantial drop in the total number of injuries since 1999. Risk has dropped consistently for near- and far-side crashes, but not for rollovers, frontal, or rear impacts. For AIS3+ injured occupants, the 16% of occupants who are unbelted make up between 45-55% of injured occupants in all crash types except for near-side. Rear occupants have a 1.7 times greater risk of AIS2+ injury in far-side impacts and 2.2 times greater risk in rear impacts compared to front seat occupants, but front occupants have 1.5 times greater risk than rear occupants in frontal crashes. The risk of AIS2+ and AIS3+ injury to all body regions generally increase with age. The proportion of AIS2+ and AIS3+ injured occupants in rollovers decreases with age. In frontal, near-side, and far-side crashes, occupants with AIS2+ injury aged 66 and greater make up a higher proportion of the injured occupants compared to their involvement crashes. Risk of AIS3+ injury is highest in pickup trucks for frontal crashes, near-side and rear crashes and in passenger cars for far-side and rollovers. Risk of AIS2+ and AIS3+ injury is highest in pickup trucks for all AIS body regions. Risk of AIS3+ injury to the pelvis and femur have dropped substantially since vehicle model years 1999-2004.National Highway Traffic Safety Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/135950/1/103253.pdfDescription of 103253.pdf : Final repor

Comparing motor-­vehicle crash risk of EU and US vehicles

This study examined the hypotheses that vehicles meeting EU safety standards perform similarly to US-­‐regulated vehicles in the US driving environment, and vice versa. The analyses used three statistical approaches to “triangulate” evidence regarding differences in crash and injury risk. Separate analyses assessed crash avoidance technologies, including headlamps and mirrors. The results suggest that when controlling for differences in environment and exposure, vehicles meeting EU standards offer reduced risk of serious injury in frontal/side crashes and have driver-­‐side mirrors that reduce risk in lane-­‐change crashes better, while vehicles meeting US standards provide a lower risk of injury in rollovers and have headlamps that make pedestrians more conspicuous.Alliance of Automobile Manufacturershttp://deepblue.lib.umich.edu/bitstream/2027.42/112977/1/103199.pd

Effects of BMI on the risk and frequency of AIS 3+ injuries in motor‐vehicle crashes

Objective: Determine the effects of BMI on the risk of serious‐to‐fatal injury (Abbreviated Injury Scale ≥ 3 or AIS 3+) to different body regions for adults in frontal, nearside, farside, and rollover crashes. Design and Methods: Multivariate logistic regression analysis was applied to a probability sample of adult occupants involved in crashes generated by combining the National Automotive Sampling System (NASS‐CDS) with a pseudoweighted version of the Crash Injury Research and Engineering Network database. Logistic regression models were applied to weighted data to estimate the change in the number of occupants with AIS 3+ injuries if no occupants were obese. Results: Increasing BMI increased risk of lower‐extremity injury in frontal crashes, decreased risk of lower‐extremity injury in nearside impacts, increased risk of upper‐extremity injury in frontal and nearside crashes, and increased risk of spine injury in frontal crashes. Several of these findings were affected by interactions with gender and vehicle type. If no occupants in frontal crashes were obese, 7% fewer occupants would sustain AIS 3+ upper‐extremity injuries, 8% fewer occupants would sustain AIS 3+ lower‐extremity injuries, and 28% fewer occupants would sustain AIS 3+ spine injuries. Conclusions: Results of this study have implications on the design and evaluation of vehicle safety systems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97212/1/20079_ftp.pd

Mutual Recognition Methodology Development

Phase 1 of the Mutual Recognition Methodology Development (MRMD) project developed an approach to statistical modeling and analysis of field data to address the state of evidence relevant to mutual recognition of automotive safety regulations. Specifically, the report describes a methodology that can be used to measure evidence for the hypothesis that vehicles meeting EU safety standards would perform similarly to US-regulated vehicles in the US driving environment, and that vehicles meeting US safety standards would perform similarly to EU-regulated vehicles in the EU driving environment. As part of the project, we assessed the availability and contents of crash datasets from the US and the EU, as well as their collective ability to support the proposed statistical methodology.The report describes a set of three statistical approaches to “triangulate” evidence regarding similarity or differences in crash and injury risk associated with EU- and US-regulated vehicles. Approach 1, Seemingly Unrelated Regression, tests whether the models are identical and will also assess the capability of the data analysis to detect differences in the models, if differences exist.Approach 2, Consequences of Best Models, uses logistic regression to develop two separate models, one for EU risk and one for US risk, as a function of a set of predictors (i.e., crash, vehicle, and occupant conditions). The two models will then be exercised on a standard population for the EU and a standard population for the US. Approach 3, Evidence for Consequences, turns the question aroundto measures the overall evidence for each of a set of possible conclusions. Each conclusion is characterized by a range of relative risk on a single population. Evidence is measured using a weighted average of likelihoods for a large group of models that produce the same outcome. That evidence is then compared using Bayes Factors