Trajectories and Outcomes of Adolescents that Ride With an Impaired Driver/Drive While Impaired

Abstract

Introduction:

For young drivers, independent transportation has been noted to offer them opportunities that can be beneficial as they enter early adulthood. However, those that choose to engage in riding with an impaired driver (RWI) and drive while impaired (DWI) over time can face negative consequences reducing such opportunities. This study examined the prospective association of identified longitudinal trajectory classes among adolescents that RWI and DWI with their later health, education, and employment in emerging adulthood.

Methods:

We analyzed all seven annual assessments (Waves, W1-W7) of the NEXT Generation Health Study, a nationally representative longitudinal study starting with 10^th grade (2009-2010 school year). Using all seven waves, trajectory classes were identified by latent class analysis with RWI (last 12 months) and DWI (last 30 days) dichotomized as ≥once = 1 vs. none = 0.

Results:

Four RWI trajectories and four DWI trajectories were identified: abstainer, escalator, decliner, and persister. For RWI and DWI trajectories respectively, 45.0% (N=647) and 76.2% (N=1,657) were abstainers, 15.6% (N=226) and 14.2% (N=337) were escalators, 25.0% (N=352) and 5.4% (N=99) were decliners, and 14.4% (N=197) and 3.8% (N=83) persisters. RWI trajectories were associated with W7 health status (χ²=13,20, p<.01) and education attainment (χ²=18.37, p<.01). Adolescent RWI abstainers reported better later health status than RWI escalators, decliners, and persisters; and decliners reported less favorable later education attainment than abstainers, escalators, and persisters. DWI trajectories showed no association with health status, education attainment, or employment.

Conclusions:

Our findings suggest the importance of later health outcomes of adolescent RWI. The mixed findings point to the need for more detailed understanding of contextual and time-dependent trajectory outcomes among adolescents engaging in RWI and DWI.

1. Introduction

Impaired driving is a major risk factor for motor vehicle crashes (MVCs), injuries, and fatalities among all age groups. In the context of young drivers, they are at higher risk of being involved in a MVCs at any blood alcohol concentration (BAC) compared to older drivers (Voas et al., 2012). In the U.S., in 2019, among young drivers alone, alcohol impaired driving accounted for 24% of the 1,603 young driver deaths (National Highway Traffic Safety Administration, 2021). Further, 82% these young drivers had a BAC ≥ 0.08 g/dL (National Highway Traffic Safety Administration, 2016a).

Despite the more recent reductions of high school teens who reported driving after drinking (2013 (10%) – 2015 (7.8%)) (Kann et al., 2016), still 16.7 % of high school students reported riding with a driver who had been drinking alcohol and 5.4% (8.9% for those ≥ 18 y/o) report driving after drinking at least once in the last 30 days (Yellman et al., 2020). In this same context, when driving after reported use of marijuana and other illicit drugs, 38% U.S. high school students report riding with an alcohol- or drug-impaired driver (RWI) in the last 12 months (Li et al., 2014a) and 13% reported driving while alcohol or drug impaired (DWI) in the last 30 days (Li et al., 2013). Previous research shows that earlier RWI is a robust predictor of future DWI in emerging adults after licensure (Harris et al., 2017; Li et al., 2014b).

Motor vehicle crashes remain the leading cause of mortality for U.S. adolescents. In 2019, nearly 2,400 U.S. teens were killed and 258,000 were in emergency rooms for injuries in motor vehicle crashes (Centers for Disease Control and Prevention (CDC) and National Center for Injury Prevention and Control (NCIPC), 2020). A substantial body of research has reported that binge drinking, drug use, or the combination of binge drinking and drug use are central risk factors for the increased trend in crashes among youth (Centers for Disease Control and Prevention (CDC) and National Center for Injury Prevention and Control (NCIPC), 2020; Elvik, 2013). This is reflected by the fact that every day, about 1 alcohol-related crash death occurs every 51 minutes and 1 in every 5 alcohol-related crash deaths occur in passengers (National Highway Traffic Safety Administration, 2016b). Illicit drug use also contributes to a considerable portion of fatal motor vehicle crashes involving adolescents (Bates et al., 2014; Drummer et al., 2004; Holmgren et al., 2005). Complicating the landscape of impaired driving crashes is the number of U.S. states passing legislation to legalize recreational marijuana use. This will plausibly continue to impact the number of impaired driving crashes among young driver age groups. Several studies have reported increased numbers of marijuana-related fatal crashes in states that legalize its use (Salomonsen-Sautel et al., 2014; Tefft and Arnold, 2020).

Extensive adolescent alcohol and drug use trajectory-related research has explored outcomes in emerging adults (e.g., health; (Parthasarathy and Weisner, 2005) employment (McMorris and Uggen, 2000) and education (Arria et al., 2011)). Despite the well-known relationship between regular and reliable access to transportation and youth having greater opportunity to thrive in health (Arcury et al., 2005), education (Kenyon, 2011), and employment (Thakuriah and Metaxatos, 2000), relatively little is known about the association of teen engagement in RWI and DWI during high school and these same domains in later emerging adulthood.

The purpose of this study was to examine identified trajectory classes of longitudinal RWI and DWI engagement among adolescents and to assess their prospective associations with emerging adulthood health, education attainment, and employment.

2. Methods

2.1. Sampling

The data were from all seven annual waves (W1-W7; 2010-2016) of the NEXT Generation Health Study, a longitudinal nationally representative cohort study starting in 10^th grade. The sampling strategy for NEXT has been previously reported (Li et al., 2014a). From W1-W7, 91% (16.27 years, se=0.03) (260 recruited students were unable to complete the study at W1 due to delayed approval from the school district), 88% (17.19 years, se=0.03), 86% (18.17 years, se=0.03), 78% (19.16 years, se=0.02), 79% (20.28 years, se=0.02), 84% (21.28 years, se=0.02), and 83% (22.64 years, se=0.03) of the full sample (N=2785) completed the survey during spring each year. African American participants were oversampled to allow for more accurate population estimates and representativeness. Parent consent and participant assent were obtained for those under age 18. After turning age 18, participants were consented as adults. The study protocol was approved by the Institutional Review Board of the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the Yale Institutional Review Board.

2.2. Outcome Variables

2.2.1. Overall physical health in emerging adulthood (W7)

Perceived overall physical health, was measured with a single question “Would you say your health is…?” with possible responses being, Excellent; Good; Fair; and Poor at W7 (Eaton et al., 2010). The four categories were dichotomized as excellent or good health vs. poor or fair health.

2.2.2. Education attainment (W7)

Education attainment was measured with one question, “What is the highest grade of regular school you have completed (or anticipate completing by the end of the current academic term)?” with seven possible responses: 1) <high school diploma; 2) high school diploma; 3) GED; 4) some college or technical school; 5) associate’s degree; 6) bachelor’s degree; and 7) graduate degree. The seven categories were collapsed into three categories: 1) ≤high school diploma or GED; 2) some college, technical school or an associate degree (Tech/Community College); 3) and ≥bachelor’s degree (4-Year College+).

2.2.3. Employment in emerging adulthood (W7)

Employment at W7 was measured with one question, “On average, what are the total hours per week you spend working in paid and/or unpaid jobs?” with possible responses: 1) None; 2) ≤5; 3) 6-10; 4) 11-15; 5) 16-20; 6) 21-25; 7) 26-30; 8) >30 hours/week. The eight categories were collapsed to four categories: 1) None; 2) ≤20; 3) 21-30; and 4) >30 hours/week.

2.3. Trajectory Variables

2.3.1. Riding with an alcohol or drug-impaired driver in the past 12 months (RWI, W1-W7)

At W1-W4, RWI was measured using one question derived from the Youth Risk Behavior Survey (YRBS) (Centers for Disease Control and Prevention, 2011) asking participants “How many times, during the last 12 months, they rode in a vehicle driven by someone else who had been drinking alcohol or using illegal drugs?” At W5-W7, RWI was measured by three questions. Participants were first asked: “During the last 12 months, how many times did you ride in a vehicle driven by someone who had been drinking alcohol?” The same question was repeated for “smoking marijuana” and “using illicit drugs other than alcohol or marijuana.” The questions at W1-W4 and W5-W7 were collapsed and dichotomized as: 1=RWI ≥once vs. 2=no RWI (last 12 months).

2.3.2. Driving while alcohol- or drug-impaired in the past 30 days (DWI, W1-W7)

At W1-W3, DWI was measured using one question derived from the YRBS questionnaire (Centers for Disease Control and Prevention, 2011) asking participants on how many days in the last 30 days they drove after drinking alcohol or using illegal drugs. At W4-W7, DWI was assessed with three items asking participants how many days they drove after drinking alcohol, smoking marijuana or using illicit drugs. The questions at W1-W3 and W4-W7 were collapsed and dichotomized as: 1=DWI ≥1 day vs. 2=no DWI (past 30 days).

2.4. Statistical Analysis

The latent classes of RWI and DWI among U.S. high school adolescents have been recently identified and related detailed analyses have been published elsewhere (Vaca et al., 2021a).

Separately for RWI and DWI, we conducted Rao-Scott Chi-square test to examine the associations between trajectory classes and outcome variables: later general health, education attainment, and employment. The computations were performed in SAS software version 9.4 (SAS Institute, Cary, NC) and PROC LCA procedure (Lanza et al., 2015; PROC LCA & PROC LTA (Version 1.3.2) [Software], 2015) taking into consideration complex sampling features.

3. Results

3.1. Riding with an impaired driver (RWI)

For RWI, 3-, 4- and 5-class models were estimated from the LCA and the 4-class model (Figure 1) was selected for the analysis (Vaca et al., 2021b). The four classes included abstainer (consistently low probability over 7 waves), escalator (low to high probability over 7 waves), decliner (high to low probability over 7 waves), and persister (consistently high probability over 7 waves). Among participants, 647 (45.0%, weighted and hereafter) were categorized as abstainer, 226 (15.6%) escalator, 352 (25.0%) decliner, and 197 (14.4%) persister (Table 1).

Figure 1.

Trajectories of riding with an alcohol or drug-impaired driver (RWI) in the past 12 months using latent class analysis

Table 1.

Frequency of outcome variables by 4-group RWI trajectories

	Abstainers		Escalators		Decliners		Persisters
	N	%#	N	%#	N	%#	N	%#	Rao- Scott χ2	P
	647	100	226	100	352	100	197	100
General Health
Fare/poor	178	28.1	76	30.2	135	46.2	63	29.1	13.20	.004
Excellent/good	469	71.9	150	69.8	217	53.8	134	70.9
Education Attainment
High school or less	167	25.4	48	14.1	115	36.9	39	18.2	18.37	.005
Tech/Community College	283	44.5	97	47.9	159	41.8	95	48.5
4-Year College+	197	30.2	81	38.0	78	21.3	63	33.3
Employment Status
None	164	23.8	43	12.0	91	24.1	48	25.0	10.18	.34
≤20 hr.	95	14.3	37	15.2	47	13.2	27	10.2
21-30 hr.	96	13.3	35	15.3	56	13.2	34	18.5
>30 hr.	292	48.7	111	57.4	158	49.5	88	46.3

Note:

^#weighted %. Tech/Community College = GED, some college or technical school, or associate’s degree; 4-Year College+ = bachelor’s degree or above.

Significant associations of RWI with general health (χ²=13.20, p=.004) and education attainment (χ²=18.37, p=.005) at W7, but not employment (χ²=10.18, p=.335), were found (Table 1). All four RWI trajectory classes were associated with higher proportions of participants who reported excellent/good health at W7. Although a greater proportion of abstainers (30.2% vs. 25.4%), escalators (38.0% vs. 14.1%), and persisters (33.3% vs. 18.2%) were more likely to attain 4-Year College+ education than high school or less; decliners were less likely to attain 4- Year College+ education (21.3%) than high school or less (36.9%).

3.2. Driving while alcohol- or drug-impaired (DWI)

For DWI, 3-, 4- and 5-class models were estimated from the LCA and the 4-class model (Figure 2) was selected for the analysis (Vaca et al., 2021b). Among participants, 1657 (76.2%) were categorized as abstainer, 337 (14%) escalator, 99 (5%) decliner, and 83 (4%) persister (Table 2).

Figure 2.

Trajectories driving while alcohol- or drug-impaired (DWI) in the past 30 days using latent class analysis

Table 2.

Frequency of outcome variables by 4-group DWI trajectories

	Abstainers		Escalators		Decliners		Persisters
	N	%#	N	%#	N	%#	N	%#	Rao- Scott χ2	P
	1657	76.2	337	14.2	99	5.4	83	3.8
General Health
Fare/poor	425	31.2	102	32.6	28	31.0	26	31.6	0.12	.99
Excellent/good	1033	68.8	216	67.4	55	69.0	48	68.4
Education Attainment
High school or lessTech/Community	426	31.3	82	21.4	20	25.7	18	17.78	8.81	.19
College	641	44.8	155	52.2	41	46.7	36	52.1
4-Year College+	383	23.9	79	26.3	22	27.6	20	30.2
Employment Status
None	333	20.8	62	17.9	15	20.0	17	20.0	6.11	.73
≤20 hr.	187	11.3	43	12.9	13	8.5	8	13.9
21-30 hr.	209	13.8	44	10.5	16	20.5	16	21.8
>30 hr.	734	54.1	170	58.8	39	51.0	33	44.3

Note:

^#weighted %. Tech/Community College = GED, some college or technical school, or associate’s degree; 4-Year College+ = bachelor’s degree or above.

General health, education attainment, and employment at W7 were not significantly associated with DWI trajectory classes (Table 2).

RWI and DWI trajectory classes were significantly correlated (chi-square =444.8, p<.001). Among DWI abstainers, escalators, decliners, and persisters, 56.7%, 37.6, 59.6%, and 73.1% were RWI abstainers, escalators, decliners, and persisters, respectively.

4. Discussion

From a prevention perspective, there is considerable need to understand the trajectories and later life implications of teens who engage in RWI and DWI This is particularly true if we are to understand how to help teens recognize and implement protective behaviors that could position them better to flourish in early adulthood. This is one of the first studies to examine the associations of emerging adulthood outcomes with established RWI and DWI behavioral trajectories (Vaca et al., 2021a). Our study results show that adolescents’ RWI trajectories were associated with later general health and education attainment. Interestingly, DWI trajectory classes were not significantly associated with adolescents’ later (i.e., 4 years after high school) general health, education attainment, or work status. These findings shed light on the complexity of these behavior and provide new information on how RWI and DWI may influence important outcomes in early adulthood that typically have considerable bearing on later life success.

We found that adolescent engagement in RWI was prospectively associated with general health status and education attainment as they emerged into early adulthood. Further, we found that RWI decliners reported more fair/poor health. It is conceivable that perhaps their reported fair/poor health was correlated with less opportunities to socialize with peers and RWI. In addition, decliners had the highest proportion of high school or less plus tech/community college, and the lowest proportion of 4-year college education. College education is related to older age at the time of marriage (Payne, 2012) and having children (Heck et al., 1997; Moore and Waite, 1977; Neels et al., 2017; Payne, 2012). It has been reported that being married may reduce RWI in young adults (Beirness, 2014; Cartwright and Asbridge, 2011; Shults et al., 2009). Therefore, it is possible that young adults with non-college levels of educational attainment have jobs (Neels et al., 2017), are married (Heck et al., 1997; Moore and Waite, 1977; Neels et al., 2017), or have children of their own at earlier stages of young adulthood (Heck et al., 1997; Moore and Waite, 1977; Neels et al., 2017), all of which could attenuate engagement in RWI as protective factors. The majority of RWI escalators were found to be employed full time. This could be related to their high rates of college completion.

Despite the association between RWI trajectory and general health status as well as education attainment, it was surprising not to find similar associations between DWI trajectory classes and the outcomes of interest. Admittedly, while there is an abundance of DWI literature and it is well understood that DWI at every age is highly associated with likelihood of crash, there is comparatively few published studies evaluating specific longitudinal trajectories of teens that DWI and their early adulthood outcomes. Further, while speculative, we believe that the absence of significant findings in the DWI group could be attributed to the relatively short period of evaluation (i.e., only 4 years after high school for this study) as well as the low prevalence of escalators (14%) and persisters (4%). Given that emerging adulthood is characterized as a distinct period in terms of development, demographics, norms, and identity explorations (Arnett, 2007), the outcomes under evaluation for a DWI trajectory class, while likely malleable, are dynamic. As such, it is plausible that significant findings (e. g., DWI involving law enforcement) in this area may take longer to manifest. A recent prospective study found that early persistent high or increasingly frequent use of marijuana from adolescent to young adulthood was associated with risks for underachievement in the domains of education, occupational success, and health in young adulthood at age 22 to 29 (Thompson et al., 2019). The timing of when the effects occurred in that study (22 - 29 y/o) compared to ours (~ 23 y/o) provides some evidence of delayed effects of DWI.

Further, although RWI and DWI share some important behavioral characteristics (e.g., alcohol/drug-associated, health-risking, involve personal transportation), they are distinct in at least one important way that could differentially influence early adulthood outcomes. That is, youth that RWI can be completely sober when engaged in this behavior, while youth alcohol/drug impairment is a prerequisite for a DWI event. We believe that supplemental assessments (retrospectively and prospectively) of our study participants, allowing for a mixed-methods approach to inquiry, could provide novel contextual insight into RWI and DWI behaviors and their respective trajectory class outcomes.

Our study has important implications for development and education as well as health-related policy (Fell et al., 2016). Youth who engage in RWI and/or DWI are a key public health concern given their vulnerability, both physically in terms of the risk for crash-injury (National Highway Traffic Safety Administration, 2017) and behaviorally, for DWI, as a result neurotoxic effects on the developing brain due to alcohol/drug use (Jones et al., 2018). Further education targeting early prevention of alcohol- and substance use disorders with intentional focus on RWI and DWI behavior in adolescents could improve outcomes of adolescents as they transition to emerging adulthood. Therefore, one goal of primary intervention in this context should be more attentive and prevention-action orientation toward better control of underage drinking as well as impaired driving during high school (i.e., reinvigorating prevention efforts) and within communities that have large adolescents populations (Fell et al., 2009). Similarly, the use of approaches to enhance prevention-focused social norms of youth, families, and communities as it applies to RWI and DWI, could yield tangible and broader reaching benefits. Moreover, policy makers should further be encouraged to revisit well-known effective state-level underage drinking laws (e.g., minimum legal drinking age, zero-tolerance) and reassess the extent of state-level implementation that could save hundreds of lives every year (Fell et al., 2016).

We recognize that our study has limitations. First, the school-based recruitment does not allow the study finding to be generalized to all youth, particularly those teens that are not enrolled in high school. Second, the study sample related to our DWI variable changed over annual assessment waves because the eligibility of driver licensure increased over the time of the study as participants aged (eligible participants W1, N = 402 - W7, N = 1877). This could have influenced related analyses. Third, RWI and DWI were measured based on different time periods (i.e., RWI: in the last 12 months; DWI: within the last month). This may have contributed to some under-reporting of RWI and DWI behavior limiting identification of relationships between RWI and DWI trajectory classes and outcome variables. Fourth, with annual wave assessment data usually collected during the springtime each year, participants were asked to report the highest grade of schooling they had completed or anticipate completing by the end of the academic term.

5. Conclusion

Adolescent RWI abstainers reported better later health status than RWI escalators, decliners, and persisters; and decliners reported less favorable 4-year college education attainment than abstainers, escalators, and persisters. Our results suggest that adolescent RWI and DWI are complex dynamic behaviors warranting both in-depth contextual (i.e., qualitative/mixed methods approach) and extended-time detailed investigation of trajectories and relationships to later health, education, and employment. These and future-related findings can inform prevention and intervention efforts aimed at preserving future beneficial opportunities, so adolescents thrive in emerging adulthood.