Motor Vehicle Mortality Research Articles

An Emerging Racial Disparity in US Motor Vehicle Fatalities

This cohort study examines an increase in motor vehicle fatality rates for Black individuals in the US beginning in 2014 compared with White individuals.

Mandatory Vision Test for Driver License Renewal in Association With Traffic Fatality and Injury Among Older Road Users in Utah.

In 2011, Utah began requiring that drivers aged 65years and older pass a vision test at each license renewal. This study aims to investigate if the mandatory vision test associated with motor vehicle fatality and injury rates in older road users. We fit controlled interrupted time series analysis models to compare fatality and injury rates for older adults (65+) affected by the law to younger adults (45-64) unaffected by the law. The models yielded estimates of differential level and slope changes in fatality and injury rates, which we used to estimate policy associations. We did not find evidence that implementing an accelerated vision test for older adults in Utah was associated with a reduction in injury or fatality rates among older (65+) drivers and non-drivers relative to those aged 45-64. Other strategies might be considered to prevent fatal motor vehicle crashes (MVCs) in older adults.

Effects of side-effect risk framing strategies on COVID-19 vaccine intentions: a randomized controlled trial.

Fear over side-effects is one of the main drivers of COVID-19 vaccine hesitancy. A large literature in the behavioral and communication sciences finds that how risks are framed and presented to individuals affects their judgments of its severity. However, it remains unknown whether such framing changes can affect COVID-19 vaccine behavior and be deployed as policy solutions to reduce hesitancy. We conducted a pre-registered randomized controlled trial among 8998 participants in the United States and the United Kingdom to examine the effects of different ways of framing and presenting vaccine side-effects on individuals' willingness to get vaccinated and their perceptions of vaccine safety. Adding a descriptive risk label ('very low risk') next to the numerical side-effect and providing a comparison to motor-vehicle mortality increased participants' willingness to take the COVID-19 vaccine by 3.0 percentage points (p=0.003) and 2.4 percentage points (p=0.049), respectively. These effects were independent and additive and combining both framing strategies increased willingness to receive the vaccine by 6.1 percentage points (p<0.001). Mechanistically, we find evidence that these framing effects operate by increasing individuals' perceptions of how safe the vaccine is. Low-cost side-effect framing strategies can meaningfully affect vaccine intentions at a population level. Heidelberg Institute of Global Health. German Clinical Trials Registry (#DRKS00025551).

Analysis of Sleep apnea Considering Electrocardiogram Data Using Deep learning Algorithms

Sleep is a vital component of every human being. Adequate restful and restorative sleep reenergizes the body, enhances overall health and psychological well-being. Sleep hygiene, chaotic lifestyles, disorder breathing, stress, and anxiety contribute to poor sleep quality. Obstructive sleep apnea (OSA) sleep respiratory disorder causes temporary lapses of breathing results in gasping, choking, snoring sounds during sleep. The individual does not consciously wake up, but the brain has to start breathing again which disrupts the sleep quality. Polysomnography (PSG) sleep study is employed to diagnose sleep disorders by using either in-home or laboratory-based comprehensive tests. The untreated OSA leads to deterioration in health, performance consequences with severity including daytime sleepiness, motor vehicle accidents, workplace errors, cardiovascular morbidity, and mortality. The pre-processed, interpolated and segmented ECG signal is considered for the examination of OSA. This paper focuses on three types of deep learning classifiers-based prediction models for detection of apnea from the ECG signal. The accuracy value of Long Short Term Memory model (LSTM) is 85 percent and classifier’s ability to distinguish between normal and apnea events is 0.88.The Gated Recurrent Unit (GRU) classifier and Convolution Neural Network (CNN) model have an f1- score value of 0.80. The proposed LSTM model provides the optimal performance in comparison to other deep learning models used for classification with respect to area under the curve (AUC) and accuracy metrics.

Lesson from the continuing 21st century motor vehicle success

In 1999, the Centers for Disease Control and Prevention proclaimed the reduction in motor vehicle fatalities to be one of the great public health achievements of the 20th century. That...

Relationship Between Smoking and Motor Vehicle Death Rates in the U.S.

Relationship Between Smoking and Motor Vehicle Death Rates in the U.S.

Machine learning prediction of blood alcohol concentration: a digital signature of smart-breathalyzer behavior

Excess alcohol use is an important determinant of death and disability. Machine learning (ML)-driven interventions leveraging smart-breathalyzer data may help reduce these harms. We developed a digital phenotype of long-term smart-breathalyzer behavior to predict individuals’ breath alcohol concentration (BrAC) levels trained on data from a smart breathalyzer. We analyzed roughly one million datapoints from 33,452 users of a commercial smart-breathalyzer device, collected between 2013 and 2017. For validation, we analyzed the associations between state-level observed smart-breathalyzer BrAC levels and impaired-driving motor vehicle death rates. Behavioral, geolocation-based, and time-series-derived features were fed to an ML algorithm using training (70% of the cohort), development (10% of the cohort), and test (20% of the cohort) sets to predict the likelihood of a BrAC exceeding the legal driving limit (0.08 g/dL). States with higher average BrAC levels had significantly higher alcohol-related driving death rates, adjusted for the number of users per state B (SE) = 91.38 (15.16), p < 0.01. In the independent test set, the ML algorithm predicted the likelihood of a given user-initiated BrAC sample exceeding BrAC ≥ 0.08 g/dL, with an area under the curve (AUC) of 85%. Highly predictive features included users’ prior BrAC trends, subjective estimation of their BrAC (or AUC = 82% without the self-estimate), engagement and self-monitoring, time since the last measure, and hour of the day. In conclusion, an ML algorithm successfully quantified a digital phenotype of behavior, predicting naturalistic BrAC levels exceeding 0.08 g/dL (a threshold associated with alcohol-related harm) with good discrimination capability. This result establishes a foundation for future research on precision behavioral medicine digital health interventions using smart breathalyzers and passive monitoring approaches.

The association between marijuana and motor vehicle crashes

IntroductionWe are just beginning to be able to detect the policy implications and public health effects of changes in marijuana laws and consumption in the United States on motor vehicle related fatalities. Most studies to date use classical regression methods to study these and are thus susceptible to both model and parameter uncertainty. This study examines the associations between marijuana and motor vehicle fatality rates taking these two issues of uncertainty into account using Bayesian sturdy-values, i.e., s-values. MethodThis study utilizes a new balanced panel dataset across all states and Washington, D.C. for the period 2010 to 2016 in the context of linear models using Bayesian s-values. It addresses the association between marijuana and alcohol consumption along with the legal environment across states and through time on crash fatalities. Other important factors such as the distractive influence of cell phones are studied. The s-value approach considers a vast number of model specifications and provides robust policy guidelines. ResultsA strong association between marijuana and alcohol use on motor vehicle crash rates is found. The statistical results are both substantial and robust, i.e., non-fragile. Other important variables include cell phone use, seat belt use, speed limit laws, and fleet modernization. ConclusionOur results have found strong evidence of a life-taking relationship between marijuana use and vehicle crashes. This suggests that policy makers recognize that legislation liberalizing marijuana use may have tragic ramifications regarding motor vehicle fatalities.

Message Appeals on an Instagram Account Promoting Seat Belt Use That Attract Adolescents and Young Adults: Elaboration-Likelihood Perspective Study.

BackgroundAdolescents and young adults demonstrate the highest rate of unrestrained motor vehicle fatalities, making the promotion of seat belt restraint a priority for public health practitioners. Because social media use among adolescents and young adults has proliferated in recent years, it is critical to explore how to use this tool to promote seat belt use among this population. Social media posts can contain various types of information within each post and this information can be communicated using different modalities.ObjectiveIn this study, based on the elaboration likelihood model, we aimed to examine how adolescents and young adults reacted to different appeals in various components of posts in the pilot of a promotion intervention on the Instagram BuckleUp4Life account.MethodsUsing thematic analysis, we examined different appeals in 3 components (photo, text, and caption) of 199 posts in BuckleUp4Life and compared the number of likes for different appeals.ResultsWe found that 6 appeals were used in the posts: rational, ego, social, fun, positive emotional, and fear appeals. The results of our study showed that in photos, fun appeals were the most popular. Rational and positive emotional appeals were the most appealing in text and captions. Regardless of the location of the components (photo, text, or captions), rational appeal was the most popular appeal.ConclusionsBased on the findings of our study, we recommend that public health practitioners utilize fun photos with rational and positive emotional appeals in text and captions rather than fear or social appeals, when promoting seat belt use through social media, especially Instagram.

The impact of state laws on motor vehicle fatality rates, 1999-2015.

Motor vehicle crash (MVC) fatalities have been declining while states passed various legislation targeting driver behaviors. This study assesses the impact of state laws on MVC fatality rates to determine which laws were effective. Publically available data were collected on driver-related motor vehicle laws, law strengths, enactment years, and numbers of verified-trauma centers. Prospective data on crash characteristics and MVC fatalities 16 years or older from Fatality Analysis Reporting System 1999 to 2015 (n = 850) were obtained. Generalize Linear Autoregressive Modeling was used to assess the relative contribution of state laws to the crude MVC fatality rate while controlling for other factors. Lowering the minimum blood alcohol content (BAC) was associated with largest declines for all ages, especially the older cohorts: 16 years to 20 years (B = 0.23; p < 0.001), 21 years to 55 years (B = 1.7; p < 0.001); 56 years to 65 years (B = 3.2; p < 0.001); older than 65 years (B = 4.1; p < 0.001). Other driving under the influence laws were also significant. Per se BAC laws accompanying a reduced BAC further contributed to declines in crude fatality rates: 21 years to 55 years (B = -0.13; p < 0.001); older than 65 years (B = -0.17; p < 0.05). Driving under the influence laws enhancing the penalties, making revocation automatic, or targeting social hosts had mixed effects by age. Increased enforcement, mandatory education, vehicle impoundment, interlock devices, and underage alcohol laws showed no association with declining mortality rates. Red light camera and seatbelt laws were associated with declines in mortality rates for all ages except for older than 65 years cohort, but speed camera laws had no effect. Graduated Driver License laws were associated with declines for 16 years to 21 years (B = -0.06; p < 0.001) only. Laws targeting specific risks (elderly, motorcycles, marijuana) showed no effect on declining MVC mortality rates during the study period. States have passed a wide variety of laws with varying effectiveness. A few key laws, specifically laws lowering allowable BAC, implementing red light cameras, and mandating seatbelt use significantly reduced MVC mortality rates from 1999 to 2015. Simply adding more laws/penalties may not equate directly to lives saved. Continued research on state laws will better inform policy makers to meet evolving public health needs in the management of MVC fatalities. Epidemiological, Level III.

Context and culture: Reasons young adults drink and drive in rural America

Montana, a large and rural U.S. state, has a motor vehicle fatality rate almost double the national average. For young adults, the alcohol-related motor vehicle fatality rate in the state is almost three times the national average. Yet little research has explored the underlying reasons that young people in rural areas drink and drive. Drawing from the theory of triadic influence (TTI) and a series of qualitative focus group discussions, the current study examined how aspects of the landscape and culture of rural America promote and hinder drinking and driving among young people. In 2015 and 2016, 72 young adults (36 females) aged 18–25 years old (mean age = 20.2) participated in 11 semi-structured focus groups in 8 rural counties in Montana. Discussions were transcribed, and two reviewers independently coded text segments. Themes were identified and an inductive explanatory model was created. The results demonstrated that aspects of the social context (e.g., peer pressure and parental modeling), rural cultural values (e.g., independence, stoicism, and social cohesion), and the legal and physical environment (e.g., minimal police presence, sparse population, and no alternative transportation) promoted drinking and driving. The results also identified salient protective factors in each of these domains. Our findings demonstrate the importance of examining underlying distal determinants of drinking and driving. Furthermore, they suggest that future research and interventions should consider the complex ways in which cultural values and environmental factors intersect to shape the risky health behaviors of rural populations.

Are low income and minority households more likely to die from traffic-related crashes?

An analysis of motor vehicle mortality is conducted using data from the Census Bureau’s National Longitudinal Mortality Study for 1980, 1990, and 2000. The likelihood of being a motor vehicle crash fatality is compared to all other causes of death and not dying within the six year follow up period of the data. Using a multinomial logistic regression, mortality associations with the socioeconomics and demographics of individuals is examined. No association is found with a greater likelihood of being a motor vehicle mortality, based on family income, ethnicity, or race. Those living in rural areas, are unemployed or disabled, and residents of southern states are more likely to be a motor-vehicle fatality. These results conflict with those of many ecological studies that assume lower income neighborhoods (and their residents) are more likely to die due to motor-vehicle crashes.

Is a driver\u2019s license age waiver worth a teen\u2019s life?

BackgroundMotor vehicle crashes are the leading cause of death for teens 14–19 years of age, with younger teen drivers at higher risk than older teens. Graduated driver licensing has been proven to reduce teen driver-related motor vehicle crashes and fatalities. Arkansas allows parents to request age waivers, which allow a teen to obtain a license for independent driving before the sixteenth birthday. The objectives of this study were to: (1) determine the prevalence of age waivers issued in Arkansas and (2) determine motor vehicle crash risks associated with 14 and 15 year old drivers.MethodsThis is a brief report on an informative query exploring risk factors related to age waivers. Publicly available databases were utilized for across state comparisons. The Web-based Injury Statistics Query and Reporting Systems (WISQARS) was utilized to calculate motor vehicle crash crude death rates. National Highway Traffic Safety Administration data were utilized to identify seat belt use rates. The Fatal Analysis Reporting System (FARS) was utilized to identify crash fatality risks for 14 and 15 year old drivers in Arkansas (N = 24). Age waiver data were obtained from the Arkansas Driver Control Administration. De-identified data on fatal crashes and rates of age waiver issuance in Arkansas for 14 and 15 year olds from 2004 through 2016 were calculated.ResultsWe reviewed crash data for 14 and 15 year old drivers in Arkansas between 2004 and 2014 to determine fatality risks. Thirty-one out of seventy-five counties in Arkansas were above the state age waiver issuance rate of 30.4 per 1000 14 to 15 year old teens. Among the four states that had similar age waivers for 14 to 15 year olds, Arkansas had the highest motor vehicle death rate of 10.2 per 100,000 young teens and the lowest seat belt use rate at 73%.ConclusionsArkansas had the highest reported teen crash fatality rates among 4 states with age waivers. The volume of age waivers issued in Arkansas is concerning. Further research is needed to understand parental motivation when asking for age waivers and their level of awareness of the risks involved.

Fatal Pediatric Motor Vehicle Crashes on U.S. Native American Indian Lands Compared to Adjacent Non-Indian Lands: Restraint Use and Injury by Driver, Vehicle, Roadway and Crash Characteristics

There are large disparities in American Indian pediatric motor vehicle (MV) mortality with reports that several factors may contribute. The Fatality Analysis Reporting System for 2000–2014 was used to examine restraint use for occupants aged 0–19 years involved in fatal MV crashes on Indian lands (n = 1667) and non-Indian lands in adjacent states (n = 126,080). SAS GLIMMIX logistic regression with random effects was used to generate odds ratios (OR) with 95% confidence intervals (CI). Restraint use increased in both areas over the study period with restraint use on Indian lands being just over half that of non-Indian lands for drivers (36.8% vs. 67.8%, p < 0.0001) and for pediatric passengers (33.1% vs. 59.3%, p < 0.0001). Driver restraint was the strongest predictor of passenger restraint on both Indian and non-Indian lands exerting a stronger effect in ages 13–19 than in 0–12 year olds. Valid licensed driver was a significant predictor of restraint use in ages 0–12 years. Passengers in non-cars (SUVs, vans and pickup trucks) were less likely to be restrained. Restraint use improved over the study period in both areas, but disparities failed to narrow as restraint use remains lower and driver, vehicle and crash risk factors higher for MV mortality on Indian lands.

Availability and consistency of health and non-health data for road traffic fatality: Analysis of data from 195 countries, 1985–2013

High-quality data are critical for validly monitoring progress toward global initiatives related to road traffic crash prevention. We assessed the availability and consistency of road traffic mortality data from health and non-health departments in national governments, plus changes in data consistency over time from 1985 to 2013. Using freely accessible data, we systematically assessed availability and consistency of health and non-health data from 1985 to 2013 in 195 countries. Data availability was reflected by the presence of data on motor vehicle mortality rates in that country at any points between 1985 and 2013. The ratio of ‘health data divided by non-health data’ was calculated to measure the consistency of the two types of data sources. We found that 77 of the 195 countries in the world (39%) had both health and non-health data sources available from 1985 to 2013. None of the 34 low-income countries had both kinds of data sources simultaneously available, while 41 of 55 high-income countries had both kinds of data sources. Of the 71 countries having both kinds of data for five years or more, 33 countries demonstrated high consistency between data sources, 25 countries showed moderate data consistency, and 13 countries displayed low consistency. Jamaica, Mexico, and China had the largest data inconsistencies. 26 of the 71 countries witnessed improved data consistency between 1985 and 2013, but nine experienced decreasing data consistency, in a few cases to a large degree. Efforts are needed to identify reasons leading to data quality changes, and to develop approaches to improve data quality in those nations where it is inadequate.

Dissemination of a Child Passenger Safety Program Through Trauma Center-Community Partnerships.

Improper child passenger restraint use contributes to higher pediatric motor vehicle collision morbidity and mortality among cultural minority populations. Child passenger safety education improves caregiver knowledge of restraint use, but effective interventions require culturally specific programming. The purpose of this study was to evaluate the effectiveness of a child passenger safety education program culturally adapted through a pediatric trauma center's community partnerships. A nonexperimental observational cohort study using program evaluation data for the child passenger safety education programs during a 24-month period. Paired pretest/posttest self-reported survey responses measured changes in caregiver knowledge and self-efficacy of restraint use. Data were analyzed by class location and by caregiver language using a paired t test and Wilcoxon's signed ranks test. A total of 1,795 paired survey responses were collected in English, Spanish, or Russian. An increase in mean knowledge scores occurred overall, with a difference in mean of 0.565 (SE = 0.022, 95% CI [0.521, 0.607]). Stratification by class site and by language reflected significant increases in median scores, but findings were variable by study group. Pretest median scores for self-efficacy of restraint use were high for all groups, but the increases in posttest medians were also significant across groups (p ≤ .001). Caregiver knowledge and self-efficacy for child passenger restraint use increased after participation in the community classes. The pediatric trauma center's community partnerships facilitated uptake and adaption of the child passenger safety education programs and increased the injury prevention outreach to minority communities.

An Analysis of Illicit Drug Use and Motor Vehicle Fatalities Using Contiguous State-Level Data

This paper analyzes the influence of illicit drug use on highway fatal outcomes by estimating regression models using data for the 48 contiguous U.S. states for the years 2009 and 2010. The models include a representative, but not exhaustive, collection of roadway fatality determinants. The impact of illicit drug usage on the motor vehicle death rate differs across age groups. There are statistically significant life-taking effects from marijuana use by the very youngest drivers. Comparable effects from the usage of cocaine and nonmedical pain relievers occur among older drivers. Negatively associated with the highway death rate and statistically significant are real per capita income and seat belt use. Statistically significant positive relationships with the rate are found for the ratio of rural to urban driving, temperature, speed limit, the percentage of older drivers, and cell phone usage. The paper provides information to policy makers at a time when state-level drug laws are changing rapidly. Evidence reported here on how drug use affects highway fatal outcomes is relevant to that discussion.

Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities

In this work, we investigate how the entry of ride-sharing services influences the rate of alcohol related motor vehicle fatalities. While significant debate has surrounded ride-sharing, limited empirical work has been devoted to uncovering the societal benefits of such services (or the mechanisms which drive these benefits). Using a difference in difference approach to exploit a natural experiment, the entry of Uber Black and Uber X into California markets between 2009 and 2014, we find a significant drop in the rate of fatalities after the introduction of Uber X. Further, results suggest that not all services have the same effect, insofar as the effect of the Uber Black car service is intermittent and manifests only in selective locations (i.e., large cities). These results underscore the importance of coupling increased availability with cost savings in order to exploit the public welfare gains offered by the sharing economy. Practical and theoretical implications are discussed.

Texting while driving: the development and validation of the distracted driving survey and risk score among young adults.

BackgroundTexting while driving and other cell-phone reading and writing activities are high-risk activities associated with motor vehicle collisions and mortality. This paper describes the development and preliminary evaluation of the Distracted Driving Survey (DDS) and score. MethodsSurvey questions were developed by a research team using semi-structured interviews, pilot-tested, and evaluated in young drivers for validity and reliability. Questions focused on texting while driving and use of email, social media, and maps on cellular phones with specific questions about the driving speeds at which these activities are performed.ResultsIn 228 drivers 18–24 years old, the DDS showed excellent internal consistency (Cronbach’s alpha = 0.93) and correlations with reported 12-month crash rates. The score is reported on a 0–44 scale with 44 being highest risk behaviors. For every 1 unit increase of the DDS score, the odds of reporting a car crash increases 7 %. The survey can be completed in two minutes, or less than five minutes if demographic and background information is included. Text messaging was common; 59.2 and 71.5 % of respondents said they wrote and read text messages, respectively, while driving in the last 30 days.ConclusionThe DDS is an 11-item scale that measures cell phone-related distracted driving risk and includes reading/viewing and writing subscores. The scale demonstrated strong validity and reliability in drivers age 24 and younger. The DDS may be useful for measuring rates of cell-phone related distracted driving and for evaluating public health interventions focused on reducing such behaviors.

The Impact of Driving Knowledge on Motor Vehicle Fatalities

This paper analyzes the influence of driving knowledge on highway safety by estimating regression models on U.S. state-level data over six years (2005 through 2010). The models incorporate a representative set of motor vehicle fatality determinants. Driving knowledge—as measured by performance on the GMAC Insurance National Drivers Test—has a statistically significant life-saving effect. Negatively related to the motor vehicle death rate and statistically significant are: real per capita income, precipitation, seat belt use, and a linear trend. Statistically significant positive associations with the rate are found for: the ratio of rural to urban driving, temperature, the percentage of young drivers, the percentage of old drivers, and alcohol consumption.