Hit-and-run or hit-and-stay? Unintended effects of a stricter BAC limit

Although they comprise a relatively small subset of all traffic deaths, hit-and-run (HR) fatalities are both contemptible and preventable. We analyze longitudinal data from 1982 to 2008 to examine the effects of blood alcohol concentration (BAC) laws on HR traffic fatalities. Our results suggest that lower BAC limits may have an unintended consequence of increasing HR fatalities, whereas a similar effect is absent for non-HR fatalities. Specifically, we find that the adoption of a 0.08 BAC limit is associated to an 8.3% increase in HR fatalities. This unintended effect is more pronounced in urban areas and during weekends, which are typical settings for HR incidents.

Drinking-and-driving in the United States from 1983-2017: comparing survey and model-based estimates of prevalence

Although several approaches to estimating prevalence and excess risk exist, each relies on behavioral assumptions that are subject to credible objections. In this article, we compare the assumptions of the most widely accepted approach over the past three decades-results from the National Roadside Survey (NRS)-with a recently revived model-based alternative that estimates these population parameters from the observed distribution of fatal motor vehicle crashes. Comparing estimates of prevalence covering the past four decades, we find that when driver non-response rates in NRS are small, estimates of the prevalence of alcohol-involved driving are nearly identical between methods, suggesting that the underlying behavior assumptions of both models approximately hold. For the past two decades, however, as the rate of driver refusal in the NRS has increased substantially, prevalence estimates between methods have diverged. A counterfactual analysis reveals that the estimates for drinking-and-driving from the model-based approach should be taken as at least as valid as those from the NRS. That is troubling as these methods yield markedly different conclusions about the continued effectiveness of existing traffic safety policy: the NRS finds that the prevalence of drinking-and-driving has fallen monotonically over time, while estimates from the model-based approach suggest that prevalence has plateaued at 15% for the past two decades. More unsettling however, is the conclusion that researchers and policy-makers may know very little about the extent of legally-impaired driving or how it has changed over time.

Validation of not-at-fault driver representativeness assumption for quasi-induced exposure using U.S. national traffic databases

INTRODUCTION

The quasi-induced exposure (QIE) method has been widely implemented into traffic safety research. One of the key assumptions of QIE method is that not-at-fault drivers represent the driving population at the time of a crash. Recent studies have validated the QIE representative assumption using not-at-fault drivers from three-or-more vehicle crashes (excluding the first not-at-fault drivers; D3_other) as the reference group in single state crash databases. However, it is unclear if the QIE representativeness assumption is valid on a national scale and is a representative sample of driving population in the United States. The aims of this study were to assess the QIE representativeness assumption on a national scale and to evaluate if D3_other could serve as a representative sample of the U.S. driving population.

METHOD

Using the Fatality Analysis Reporting System (FARS) and the National Occupant Protection Use Survey (NOPUS), distributions of driver gender, age, vehicle type, time, and roadway type among the not-at-fault drivers in clean two-vehicle crashes, the first not-at-fault drivers in three-or-more-vehicle crashes, and the remaining not-at-fault drivers in three-or-more vehicle crashes were compared to the driver population observed in NOPUS.

RESULTS

The results showed that with respect to driver gender, vehicle type, time, and roadway type, drivers among D3_other did not show statistical significant difference from NOPUS observations. The age distribution of D3_other driver was not practically different to NOPUS observations.

CONCLUSIONS

Overall, we conclude that D3_other drivers in FARS represents the driving population at the time of the crash. Practical applications: Our study provides a solid foundation for future studies to utilize D3_other as the reference group to validate the QIE representativeness assumption and has potential to increase the generalizability of future FARS studies.

Multiple imputation of missing marijuana data in the Fatality Analysis Reporting System using a Bayesian multilevel model

BACKGROUND

The Fatality Analysis Reporting System (FARS) provides important data for studying the role of marijuana in motor vehicle crashes. However, marijuana testing data are available for only 34% of drivers in the FARS, which represents a major barrier in the use of the data.

METHODS

We developed a multiple imputation (MI) procedure for estimating marijuana positivity among drivers with missing marijuana test results, using a Bayesian multilevel model that allows a nonlinear association with blood alcohol concentrations (BACs), accounts for correlations among drivers in the same states, and includes both individual-level and state-level covariates. We generated 10 imputations for the missing marijuana-testing data using Markov chain Monte Carlo simulations and estimated positivity rates of marijuana in the nation and each state.

RESULTS

Drivers who were at older age, female, using seatbelt at the time of crash, having valid license, or operating median/heavy trucks were less likely to test positive for marijuana. There was a reverse U-shaped association between BACs and positivity of marijuana, with lower positivity when BACs < 0.01 g/dL or ≥0.15 g/dL. The MI data estimated a lower positivity rate of marijuana in the nation and each of the state than the observed data, with a national positivity rate of 11.7% (95% CI: 11.1, 12.4) versus 14.8% using the observed data in 2013.

CONCLUSIONS

Our MI procedure appears to be a valid approach to addressing missing marijuana data in the FARS and may help strengthen the capacity of the FARS for monitoring the epidemic of drugged driving and understanding the role of marijuana in fatal motor vehicle crashes in the United States.

Factors Associated with Pediatric Mortality from Motor Vehicle Crashes in the United States: A State-Based Analysis

OBJECTIVE

To examine geographic variation in motor vehicle crash (MVC)-related pediatric mortality and identify state-level predictors of mortality.

STUDY DESIGN

Using the 2010-2014 Fatality Analysis Reporting System, we identified passengers <15 years of age involved in fatal MVCs, defined as crashes on US public roads with ≥1 death (adult or pediatric) within 30 days. We assessed passenger, driver, vehicle, crash, and state policy characteristics as factors potentially associated with MVC-related pediatric mortality. Our outcomes were age-adjusted, MVC-related mortality rate per 100 000 children and percentage of children who died of those in fatal MVCs. Unit of analysis was US state. We used multivariable linear regression to define state characteristics associated with higher levels of each outcome.

RESULTS

Of 18 116 children in fatal MVCs, 15.9% died. The age-adjusted, MVC-related mortality rate per 100 000 children varied from 0.25 in Massachusetts to 3.23 in Mississippi (mean national rate of 0.94). Predictors of greater age-adjusted, MVC-related mortality rate per 100 000 children included greater percentage of children who were unrestrained or inappropriately restrained (P < .001) and greater percentage of crashes on rural roads (P = .016). Additionally, greater percentages of children died in states without red light camera legislation (P < .001). For 10% absolute improvement in appropriate child restraint use nationally, our risk-adjusted model predicted >1100 pediatric deaths averted over 5 years.

CONCLUSIONS

MVC-related pediatric mortality varied by state and was associated with restraint nonuse or misuse, rural roads, vehicle type, and red light camera policy. Revising state regulations and improving enforcement around these factors may prevent substantial pediatric mortality.

Work-related fatal motor vehicle traffic crashes: Matching of 2010 data from the Census of Fatal Occupational Injuries and the Fatality Analysis Reporting System

Motor vehicle traffic crashes (MVTCs) remain the leading cause of work-related fatal injuries in the United States, with crashes on public roadways accounting for 25% of all work-related deaths in 2012. In the United States, the Bureau of Labor Statistics (BLS) Census of Fatal Occupational Injuries (CFOI) provides accurate counts of fatal work injuries based on confirmation of work relationship from multiple sources, while the National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) provides detailed data on fatal MVTCs based on police reports. Characterization of fatal work-related MVTCs is currently limited by data sources that lack either data on potential risk factors (CFOI) or work-relatedness confirmation and employment characteristics (FARS). BLS and the National Institute for Occupational Safety and Health (NIOSH) collaborated to analyze a merged data file created by BLS using CFOI and FARS data. A matching algorithm was created to link 2010 data from CFOI and FARS using date of incident and other case characteristics, allowing for flexibility in variables to address coding discrepancies. Using the matching algorithm, 953 of the 1044 CFOI "Highway" cases (91%) for 2010 were successfully linked to FARS. Further analysis revealed systematic differences between cases identified as work-related by both systems and by CFOI alone. Among cases identified as work-related by CFOI alone, the fatally-injured worker was considerably more likely to have been employed outside the transportation and warehousing industry or transportation-related occupations, and to have been the occupant of a vehicle other than a heavy truck. This study is the first step of a collaboration between BLS, NHTSA, and NIOSH to improve the completeness and quality of data on fatal work-related MVTCs. It has demonstrated the feasibility and value of matching data on fatal work-related traffic crashes from CFOI and FARS. The results will lead to improvements in CFOI and FARS case capture, while also providing researchers with a better description of fatal work-related MVTCs than would be available from the two data sources separately.

Bicycle helmet laws are associated with a lower fatality rate from bicycle-motor vehicle collisions

OBJECTIVE

To assess the association between bicycle helmet legislation and bicycle-related deaths sustained by children involved in bicycle-motor vehicle collisions.

STUDY DESIGN

We conducted a cross-sectional study of all bicyclists aged 0-16 years included in the Fatality Analysis Reporting System who died between January 1999 and December 2010. We compared fatality rates in age-specific state populations between states with helmet laws and those without helmet laws. We used a clustered Poisson multivariate regression model to adjust for factors previously associated with rates of motor vehicle fatalities: elderly driver licensure laws, legal blood alcohol limit (<0.08% vs ≥ 0.08%), and household income.

RESULTS

A total of 1612 bicycle-related fatalities sustained by children aged <16 years were evaluated. There were no statistically significant differences in median household income, the proportion of states with elderly licensure laws, or the proportion of states with a blood alcohol limit of >0.08% between states with helmet laws and those without helmet laws. The mean unadjusted fatality rate was lower in states with helmet laws (2.0/1,000,000 vs 2.5/1,000,000; P = .03). After adjusting for potential confounding factors, lower fatality rates persisted in states with mandatory helmet laws (adjusted incidence rate ratio, 0.84; 95% CI, 0.70-0.98).

CONCLUSION

Bicycle helmet safety laws are associated with a lower incidence of fatalities in child cyclists involved in bicycle-motor vehicle collisions.