Residence location of drivers involved in fatal crashes

Traffic safety researchers have long known that the majority of fatal crashes occur on rural roads, but it is not clear whether these crashes involve people who live in rural areas or residents of urban areas traveling on rural roads. ‘Geodemographic' market-research tools allow determination of the urbanization of drivers' residence locations from their postal ‘zip code.' Using data from the 1988–1992 files of the Fatal Accident Reporting System (FARS) maintained by the National Highway Traffic Safety Administration (NHTSA), this study determined the residence location of several subgroups of drivers involved in fatal crashes. Not only did the majority of fatal crashes occur in rural areas, but the majority of fatal crashes involved rural and small-town residents, and the majority of the rural and small-town residents involved in fatal crashes were traveling on rural roads.     

Differences in state drug testing and reporting by driver type in U.S. fatal traffic crashes

IntroductionDriving under the influence of drugs, including marijuana, has become more prevalent in recent years despite local, state, and federal efforts to prevent such increases. The Fatality Analysis Reporting System (FARS) is the primary source of drugged driving data for fatal crashes in the United States but lacks the completeness required to calculate unbiased estimates of drug use among drivers involved in fatal crashes.MethodsThis article uses the 2013 FARS dataset to present differences in state drug testing rates by driver type, driver fault type, and state-level factors; discusses limitations related to analysis and interpretation of drugged driving data; and offers suggestions for improvements that may enable appropriate use of FARS drug testing data in the future.ResultsResults showed that state drug testing rates were highest among drivers who died at the scene of the crash (median = 70.8%) and drivers who died and were at fault in the crash (median = 64.4%). The lowest testing rates were seen among surviving drivers who were not transported to a hospital (median = 14.0%) and surviving drivers who were not at fault in the crash (median = 10.0%). Drug testing rates differed by state blood alcohol content (BAC) testing rate across all driver types and driver fault types, and in general, states that tested a higher percentage of drivers for BAC had higher drug testing rates.DiscussionTesting rates might be increased through standardization and mandatory testing policies. FARS data users should continue to be cautious about the limitations of using currently available data to quantify drugged driving. More efforts are needed to improve drug testing and reporting practices, and more research is warranted to establish drug concentration levels at which driving skills become impaired.     

Predictors of injury among younger and older adults in fatal motor vehicle crashes

With 2003 Fatality Analysis Reporting System data, we examined relationships among predictors of motor vehicle injury/fatality outcomes for younger (35–54 years) and older (65 years and older) drivers. Using the Precede-Proceed Model of Health Promotion as an organizing framework, we classified variables into person, vehicle and environment domains and conducted a multinomial logistic regression.Significant risk factors for older driver injuries were impact crashes at 1–3 o’clock (OR = 1.65; CI: 1.05–2.59), 7–9 o’clock angles (OR = 2.59; CI = 1.45–4.63), and driving with one passenger (OR = 2.25; CI: 1.58–3.20). Previous other motor vehicle convictions were significantly associated with reduced risk of injury (OR = 0.55; CI = 0.34–0.90). The 7–9 o’clock angle (OR = 3.06; CI: 1.83–5.12), and driving in daylight hours were risk factors for fatality among older drivers.Many risk factors (e.g., female gender, non-seatbelt use, rollover crashes, and vehicle body type), and protective factors (e.g., number of lanes and non-airbag deployment) were relevant for younger and older drivers. Findings showed relevant factors for drivers from both age groups, with some pointing to older adults, and set the stage for further research to develop injury and fatality prevention programs.     

Identify sequence of events likely to result in severe crash outcomes

The current practice of crash characterization in highway engineering reduces multiple dimensions of crash contributing factors and their relative sequential connections, crash sequences, into broad definitions, resulting in crash categories such as head-on, sideswipe, rear-end, angle, and fixed-object. As a result, crashes that are classified in the same category may contain many different crash sequences. This makes it difficult to develop effective countermeasures because these crash categorizations are based on the outcomes rather than the preceding events. Consequently, the efficacy of a countermeasure designed for a specific type of crash may not be appropriate due to different pre-crash sequences. This research seeks to explore the use of event sequence to characterize crashes. Additionally, this research seeks to identify crash sequences that are likely to result in severe crash outcomes so that researchers can develop effective countermeasures to reduce severe crashes. This study utilizes the sequence of events from roadway departure crashes in the Fatality Analysis Reporting System (FARS), and converts the information to form a new categorization called “crash sequences.” The similarity distance between each pair of crash sequences were calculated using the Optimal Matching approach. Cluster analysis was applied to group crash sequences that are etiologically similar in terms of the similarity distance. A hybrid model was constructed to mitigate the potential sample selection bias of FARS data, which is biased toward more severe crashes. The major findings include: (1) in terms of a roadway departure crash, the crash sequences that are most likely to result in high crash severity include a vehicle that first crosses the median or centerline, runs-off-road on the left, and then collides with a roadside fixed-object; (2) seat-belt and airbag usage reduces the probability of dying in a roadway departure crash by 90%; and (3) occupants who are seated on the side of the vehicle that experience a direct impact are 2.6 times more likely to die in a roadway departure crash than those not seated on the same side of the vehicle where the impact occurs.     

Do states upgrading to primary enforcement of safety belt laws experience increased daytime and nighttime belt use?

As of 1 January 2007, 26 states and the District of Columbia have enacted primary enforcement of their safety belt laws, which allows law enforcement to stop motorists and cite them solely when they observe a vehicle occupant who is not wearing a safety belt. Interrupted time series analyses were used to determine whether six states which upgraded to primary enforcement laws experienced changes in nighttime (9:00 p.m. to 4:59 a.m.) and daytime (5:00 a.m. to 8:59 p.m.) safety belt use based on proxy estimates from fatal crash-involved vehicle occupants. Nighttime and daytime safety belt use increased in five of the six states after the primary enforcement laws were enacted. Because the methods used in these analyses reduced the likelihood that these increases resulted from preexisting secular trends towards increased belt use, the results provide strong support that upgrading from secondary to primary enforcement increases occupant safety belt use during both daytime and nighttime periods.     

Is the number of traffic fatalities in American hospitals decreasing?

Fatality Analysis Reporting System (FARS) data from 1977 to 2002 record a decreasing number of traffic fatalities taken to a hospital compared with traffic fatalities not taken to a hospital. In this study, we calculated the proportions of decedents reportedly taken to a hospital each year in each state, and the proportions surviving at least 1 h. We also used death certificate data from the National Center for Health Statistics (NCHS) for 1979-1999 to categorize the proportion of motor vehicle fatalities in each state by hospital patient status. The annual number of traffic fatalities decreased slightly over the period of observation. The proportion of decedents recorded in FARS as transported to a hospital fell from about 73 to 43%. However, this proportion decreased abruptly at certain times in some states, suggesting previous misclassification. The proportion surviving at least 1 h remained relatively constant. NCHS data showed a decline in the proportion declared dead in hospitals from 62 to 51%, including a decline in the proportion declared dead on arrival (DOA) from 20 to 8%. Along with occasional misclassification in some states, the decrease in cases transported only to be pronounced DOA could explain why FARS data show a decrease in deaths after hospital transport.     

Pooling data from fatality analysis reporting system (FARS) and generalized estimates system (GES) to explore the continuum of injury severity spectrum

Fatality Analysis Reporting System (FARS) and Generalized Estimates System (GES) data are most commonly used datasets to examine motor vehicle occupant injury severity in the United States (US). The FARS dataset focuses exclusively on fatal crashes, but provides detailed information on the continuum of fatality (a spectrum ranging from a death occurring within thirty days of the crash up to instantaneous death). While such data is beneficial for understanding fatal crashes, it inherently excludes crashes without fatalities. Hence, the exogenous factors identified as critical in contributing (or reducing) to fatality in the FARS data might possibly offer different effects on non-fatal crash severity levels when a truly random sample of crashes is considered. The GES data fills this gap by compiling data on a sample of roadway crashes involving all possible severity consequences providing a more representative sample of traffic crashes in the US. FARS data provides a continuous timeline of the fatal occurrences from the time to crash – as opposed to considering all fatalities to be the same. This allows an analysis of the survival time of victims before their death. The GES, on the other hand, does not offer such detailed information except identifying who died in the crash. The challenge in obtaining representative estimates for the crash population is the lack of readily available “appropriate” data that contains information available in both GES and FARS datasets. One way to address this issue is to replace the fatal crashes in the GES data with fatal crashes from FARS data thus augmenting the GES data sample with a very refined categorization of fatal crashes. The sample thus formed, if statistically valid, will provide us with a reasonable representation of the crash population.This paper focuses on developing a framework for pooling of data from FARS and GES data. The validation of the pooled sample against the original GES sample (unpooled sample) is carried out through two methods: (1) univariate sample comparison and (2) econometric model parameter estimate comparison. The validation exercise indicates that parameter estimates obtained using the pooled data model closely resemble the parameter estimates obtained using the unpooled data. After we confirm that the differences in model estimates obtained using the pooled and unpooled data are within an acceptable margin, we also simultaneously examine the whole spectrum of injury severity on an eleven point ordinal severity scale – no injury, minor injury, severe injury, incapacitating injury, and 7 refined categories of fatalities ranging from fatality after 30 days to instant death – using a nationally representative pooled dataset. The model estimates are augmented by conducting elasticity analysis to illustrate the applicability of the proposed framework.     

Predicted effect of automatic crash notification on traffic mortality

OBJECTIVE: To estimate the reduction in traffic mortality in the United States that would result from an automatic crash notification (ACN) system. METHODS: 1997 Fatality Analysis Reporting System (FARS) data from 30,875 cases of incapacitating or fatal injury with complete information on emergency medical services (EMS) notification and arrival times were analyzed considering cases at any time to be in one of four states: (1) alive prior to notification; (2) alive after notification; (3) alive after EMS arrival; and (4) dead. For each minute after the crash, transition probabilities were calculated for each possible change of state. These data were used to construct models with (1) number of incapacitating injuries ranging from FARS cases up to an estimated total for the US in 1997; (2) deaths equal to FARS total; (3) transitions to death from other states proportional to FARS totals and rates and (4) other state transitions equal to FARS rates. The outcomes from these models were compared to outcomes from otherwise identical models in which all notification times were set to 1 min. RESULTS: FARS data estimated 12,823 deaths prior to notification, 1800 after notification, and 14,015 between EMS arrival and 6 h. If notification times were all set to 1 min, a model using FARS data only predicted 10,703 deaths prior to notification, 2,306 after notification, and 15,208 after EMS arrival, while a model using an estimated total number of incapacitating injuries for the US predicted 9,569 deaths prior to notification, 2,261 after notification, and 15,134 after arrival. In the first model, overall mortality was reduced from 28,638 to 28,217 (421 per year. or 1.5%), while in the second model mortality was reduced to 26,964 (1,674 per year, or 6%). CONCLUSIONS: Modest but important reduction in traffic mortality should be expected from a fully functional ACN system. Imperfect systems would be less effective.