Teen driver crash risk and associations with smoking and drowsy driving

Accident Analysis and Prevention 40 (2008) 869–876

Teen driver crash risk and associations with smoking and drowsy driving Lauren Hutchens a , Teresa M. Senserrick a , Patrick E. Jamieson b , Dan Romer b , Flaura K. Winston a,c,d,∗ a

Center for Injury Research and Prevention (formerly TraumaLink), The Children’s Hospital of Philadelphia, Philadelphia, PA, USA b Adolescent Risk Communication Institute, Annenberg Public Policy Center, University of Pennsylvania, Philadelphia, PA, USA c Division of General Pediatrics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA d Leonard Davis Institute for Health Economics, University of Pennsylvania, Philadelphia, PA, USA Received 7 February 2007; received in revised form 8 August 2007; accepted 3 October 2007

Abstract Motor vehicle crashes are the leading cause of death for young people in the United States. The goal of this study was to identify risk factor profiles of teen and young adult drivers involved in crashes. General demographic and behavioral as well as driving-related factors were considered. Analysis of a nationally representative telephone survey of U.S. young drivers ages 14 to 22 (N = 900) conducted in 2005 was restricted to 506 licensed drivers (learners excluded). Statistically significant univariate associations between factors of interest and the primary outcome, crash involvement (ever) as a driver, were identified and included within a multivariate logistic regression model, controlling for potential demographic confounders. Aside from length of licensure, only driving alone while drowsy and being a current smoker were associated with having been in a crash. Gaining a better understanding of these behaviors could enhance the development of more customized interventions for new drivers. © 2008 Elsevier Ltd. All rights reserved. Keywords: Smoking; Fatigued driving; Teen driver; Adolescent; Crash risk; Sleep

1. Introduction Although motor vehicle crashes (MVCs) are the leading cause of death for young people in the U.S. (Subramanian, 2006), profiles of young drivers at risk for crashes are not well described. Conversely, profiles for youth and young adults at risk for other adverse behaviors (for example, substance abuse or unprotected sexual intercourse) have been defined and interventions have been developed for these high-risk populations. Studies that have examined determinants of risk behaviors in youth, such as smoking and alcohol consumption, emphasize the roles of socioeconomic, psychological, and circumstantial life antecedents, including a range of potentially modifiable risk factors that should be taken into account for targeted prevention programs (La Rosa et al., 2004; Ellickson et al., 2004; Kirby, 2002). Likewise, driving risk is associated with non-driving specific behavioral and lifestyle factors (Shope, 2006; Bina et al.,

∗

Corresponding author at: Center for Injury Research and Prevention, The Children’s Hospital of Philadelphia, 3535 Market Street, 11th Floor, Philadelphia, PA 19104, USA. Tel.: +1 215 590 3118; fax: +1 215 590 5425. E-mail address: [email protected] (F.K. Winston). 0001-4575/$ – see front matter © 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2007.10.001

2006). Such analyses are limited; however, when such factors are considered in isolation (Connor et al., 2002; O’Malley and Johnston, 2003) or do not specifically examine risk among young drivers as opposed to drivers of all ages (Arthur et al., 2001; Petridou and Moustaki, 2000). Studies of youth risk behaviors have demonstrated that risky behaviors co-vary and are predicted by underlying tendencies toward sensation seeking, impulsive decision making, and low parental supervision (Romer, 2003; Zuckerman, 1994). It is hypothesized, therefore, that risk factors for crashes will co-vary with other youth risk behaviors. Building on the previous, often separate literatures of crash risk and other youth risk behaviors, the current study explored simultaneously a range of driving-specific and non-drivingspecific risks with the goal of identifying a risk factor profile for young drivers involved in crashes. 2. Methods 2.1. Participants and procedures A driving specific module was added to the 2005 cycle of the National Annenberg Survey of Youth (NASY), an annual

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national telephone survey of U.S. youth ages 14 to 22 developed by the Annenberg Public Policy Center at the University of Pennsylvania. The NASY uses random digit dialing telephone procedures to obtain a nationally representative sample. A description of the development of the NASY has been published previously (Romer, 2003). The survey is reviewed and approved by the Institutional Review Board of the University of Pennsylvania each year. Telephone surveys were conducted between April 28 and August 25, 2005, by a professional survey research firm (Shulman, Ronca, and Bucuvalas, Inc.). Parents or guardians of youth under the age of 18 were asked for permission to interview their child. In households with more than one eligible young person, the individual with the most recent birthday was selected. The interview was given in Spanish for those households with Spanish-speaking youth (4%). Of the 9421 households screened, 1410 (15%) had an eligible respondent, and of these 900, (64%) completed the interview. Taking into account those households that could not be screened (but may have had an eligible respondent), the overall response rate was 45.7%, comparable to national telephone health surveys of adults conducted by the Centers for Disease Control and Prevention (2003). The analysis for this study was restricted to those respondents who were drivers, excluding learners (N = 506); their characteristics are displayed in Table 1. Two in five reported having been in a crash as a driver in their lifetime. One-fourth of respondents were current smokers and nearly half were current drinkers. Seventy percent reported driving with teen passengers at least sometimes. 2.2. Measures 2.2.1. Standard NASY items The questions focused on risk-taking propensity and its manifestations in risky behaviors, many of them related to risky driving and, potentially, to crash involvement. In addition to those mentioned above, additional demographics included neighborhood household income, as computed from the average reported for the respondent’s ZIP code, and academic performance, as assessed via the question “What is your approximate letter grade average in the school you currently attend?”, asked of those who reported currently being in school. Drinking, gambling, marijuana use, seatbelt non-use, and smoking each were assessed based on positive responses to “Have you ever done any of the following?” If the response was “Yes” for any of these behaviors, the question was repeated with reference to activity in the last 30 days. For this analysis, “current use” was dichotomized as use in last 30 days versus non-use. Sensation seeking was measured using the four-item Brief Sensation Seeking Scale (Stephenson et al., 2003). This subscale includes the items: “I would like to explore strange places”; “I like to do frightening things”; “I like new and exciting experiences, even if I have to break the rules”; and “I prefer friends who are exciting and unpredictable,” with possible responses “strongly agree, somewhat agree, somewhat disagree,

Table 1 Demographic characteristics of the sample of drivers (unweighted n = 506) Variable

Value

Age (years)

14 15 16 17 18 19 20 21 22

N 1 (0.3) 2 (0.5) 38 (6.9) 67 (11.9) 77 (15.1) 93 (19.7) 99 (20.8) 72 (14.1) 57 (10.9)

Gender

Male

270 (53.4)

Race

White Black Asian Other

386 (77.4) 43 (11.8) 9 (1.5) 51 (9.4)

Ethnicity

Non-Hispanic

446 (88.9)

GPA

A B C

126 (36.0) 181 (52.7) 40 (11.3)

Current smoker Current gambler Current drinker Current marijuana user

Yes Yes Yes Yes

128 (23.3) 91 (35.1) 247 (48.9) 62 (11.4)

Hours sleep/night

≤5 6 7 8 9 10+

73 (13.7) 113 (22.4) 127 (26.1) 139 (27.3) 36 (6.7) 16 (3.9)

Sensation seeking Impulsivity

High High

201 (39.0) 172 (33.3)

Independent license type

Full Intermediate School/other

448 (89.4) 45 (8.2) 13 (2.4)

Length licensure (years)

0 1 2 3 4 5 6 7 8

49 (9.6) 102 (20.3) 82 (16.0) 87 (17.8) 84 (18.4) 54 (9.3) 30 (6.3) 10 (1.8) 3 (0.6)

Hours driven/week

0 1–2 3–5 6–8 9–12 13–15 16–30 31–50 51–84

19 (3.7) 46 (9.0) 119 (23.2) 82 (16.1) 94 (18.3) 38 (7.3) 73 (14.2) 34 (5.2) 6 (1.4)

Drives with teen passengers Driven unbelted ever Driven unbelted last month Crash as driver Drives drowsy alone Drives drowsy with teens Drives unbelted alone Drives unbelted with teens Drives alone after using drink/drug Drives with teens after using drink/drug

Yes–at least 1 h/week Yes Yes Yes Almost always/sometimes Almost always/sometimes Almost always/sometimes Almost always/sometimes Almost always/sometimes Almost always/sometimes

358 (71.4) 357 (70.8) 182 (51.5) 202 (40.7) 103 (21.6) 56 (17.9) 99 (20.8) 69 (19.8) 20 (4.2) 12 (3.6)

Values in parentheses are weighted percentages.

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and strongly disagree.” The scores were cut at the median of the total of these items for the univariate analysis; for the multivariate model it was included as a continuous variable. The scale had an alpha of .67. Details of additional NASY items can be found in Romer (2003). 2.2.2. Additional 2005 NASY items A measure of inability to delay gratification was adapted from the monetary choice procedure of Green et al. (1994). Specifically, we asked participants: “Suppose you had an offer to get paid $1000 for doing a job if you could wait six months from completion of the job to get paid. If you didn’t want to wait the six months, you could get $500 as soon as you finished. Would you accept the $500 as payment right away or wait for the $1000 in six months?” Respondents who accepted the $500 were asked if they would accept an amount lower than $500 in $100 decrements. The lowest amount they would accept was taken as their delay gratification value. A comparable procedure with successively increasing values was used for those who would not accept $500. Similar monetary choice procedures have been used with youth of this age group (Duckworth and Seligman, 2005; Reynolds et al., 2004), and it has been found to correlate with use of drugs, such as cigarettes and alcohol (Bickel and Marsch, 2001; Kollins, 2003). A second measure of impulsivity, future orientation, was assessed by 3 items from the 13-item Time Perspective Questionnaire (Fong and Hall, 2003), a scale that has also been found to correlate with drug use. Respondents rated their agreement on a 4-point scale from 4 = strongly agree to 1 = strongly disagree with two items positively scored, “Living for the moment is more important than planning for the future,” and “I spend a lot more time thinking about today than thinking about the future” and one item reverse-scored, “I have a good sense of what my long-term priorities are in life.” We created a composite impulsivity score for each participant by standardizing each of the three future orientation items plus the delay of gratification score, and then calculating the average. The internal reliability of this composite score (coefficient alpha) using an ordinal measure of association was .48. A mean split was used to dichotomize this variable into high and low categories. 2.2.3. New driving module items Additional items focused on license status, length of licensure, weekly driving hours, risky driving behaviors/conditions, and crash involvement as a driver. License status was determined by response to the item, “Do you have a permit or license to drive a car or other passenger vehicle?” Those who said “yes” were then asked to identify which license category applied: Minor School License; Instructional or Learner’s permit; Intermediate license; Full license; Motorcycle; Truck/Heavy vehicle; Other (e.g., boating or international license). Length of licensure was determined by the item “at what age did you get your first driver’s license,” recorded in years. Average weekly driving hours was calculated by responses to “thinking of an average week, about how many hours do you drive a car

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or truck for any purpose?” Respondents were also asked this question with regards to average hours driven per week with passengers between the ages of 14–19. Variables related to driving risks included how often the respondent reported “when driving alone, how often” they drove drowsy, unbelted, and after drinking or using drugs, with response options “almost always,” “some of the time,” or “rarely or never.” These items were then repeated in relation to how often they occurred “when driving with one or more passengers between the ages of 14–19.” For analyses, these variables were dichotomized as almost always or sometimes versus rarely or never. The main outcome measure, crash involvement as a driver, was dichotomized, as assessed by “yes” and “no” responses to the question, “Have you ever been in a car accident in which you were the driver?” Because the main dependent variable of interest was crash involvement as a driver, non-drivers (n = 319, 35.4%) and learner drivers (n = 75, 8.3%), whose driving behavior could not be considered independent of their adult supervisory drivers, were excluded from analyses. The final sample included 506 participants. 2.3. Statistical analysis All analyses were conducted using SPSS 13.0 for Windows (SPSS Inc., Chicago, IL). Univariate associations between crash outcome and variables of interest, including risk behaviors and demographic characteristics, were examined using chi-square tests for categorical variables and t-tests or non-parametric tests for continuous variables. Results of the univariate analyses were weighted to represent national proportions for age, gender race/ethnicity, and region of the country as defined by the U.S. Bureau of the Census (2001). Variables with an association of p ≤ .10 with crash involvement were considered for inclusion in a multivariate logistic regression model to determine the independent association between selected factors and crash involvement as a driver (unweighted). If Pearson’s correlations demonstrated high collinearity (>.80) between variable pairs, only one of each pair was included in the model. A forward stepwise method was then used to identify the strongest independent associations with crash involvement. Demographic characteristics (race, income, region, etc.) were entered as a block into the model on the first step, followed by the other variables of interest, which were entered in succession with probabilities for entry and removal set to 0.10 and 0.05, respectively. Results of logistic regression modeling were examined as unadjusted and adjusted odds ratios (OR) with corresponding 95% confidence intervals (CI). 3. Results A total of 468 males and 432 females completed the survey with a mean age of 17.8 years (14–22). A little over a third of the respondents lived in the South, while the remainder was somewhat equally spread across the Northeast, West, and Midwest regions. Nearly half of the sample was from a suburban area,

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Table 2 Characteristics of young drivers involved in crashes versus never involved – association with crash involvement (categorical variables) Characteristic

Crash involved (weighted%)

OR (unadjusted)

95% CI

p-Value*

Gender – male Length licensure <2 years Ethnicity (non-Hispanic) Current smoker Current drinker Current gambler Current marijuana user Hours sleep <8/night Grades – A-B average High sensation seeking Race (White) Believes will not live much >30 years Impulsivity (higher)

41.0 27.4 32.8 55.6 48.3 41.1 53.3 44.5 39.4 44.0 42.3 45.9 39.8

1.020 0.590 0.788 1.544 1.443 1.016 1.364 1.284 0.838 1.334 1.196 1.139 0.966

0.830, 1.253 0.447, 0.778 0.537, 1.158 1.259, 1.892 1.169, 1.780 0.778, 1.326 1.049, 1.773 1.025, 1.607 0.608, 1.155 1.087, 1.637 0.925, 1.547 0.790, 1.643 0.774, 1.204

.860 .000 .206 .000 .001 .909 .037 .029 .357 .008 .184 .603 .779

Drives with teen passengers Drives drowsy alone Drives drowsy with teens Drives unbelted alone Drives unbelted with teens Drives after drink/drug alone Drives after drink/drug with teens

40.4 53.8 39.4 43.4 43.8 50.0 23.1

0.972 1.364 0.956 1.022 1.092 1.180 1.180

0.773, 1.223 1.097, 1.696 0.689, 1.328 0.794, 1.317 0.813, 1.467 0.752, 1.853 0.752, 1.853

.841 .010 .890 .909 .596 .500 .254

*

Comparison between crash-involved and non crash-involved drivers.

while 30% were from urban areas and 20% from rural areas. The sample matched the U.S. Bureau of the Census estimates (2001) within 1 percentage point for gender and region of the country and tended to over-represent youth with less than a high school diploma (63% vs. 60%). The survey showed close agreement to race and ethnicity percentages, with slight over-representation of White non-Hispanic youth (67% vs. 64%) and slight underrepresentation of Black (10% vs. 14%), Hispanic (15% vs. 16%) and Asian youth (2% vs. 4%). Because this survey reached young people not currently in school (n = 196, or 21.8% of the sample), it can be considered more representative of this age cohort as a whole and likely more representative of higher-risk youth. Table 2 displays the results of the tests of association between categorical driver characteristics and crash involvement, while Table 3 shows these results for continuous variables. Those who had held a license for less than two years were 40% less likely to have had a crash than those who had been licensed two years or longer. Smokers were 50% more likely to have had a crash than non-smokers, and drinkers were nearly 45% more likely to have had a crash than non-drinkers. High sensation seekers were more than one-third more likely to have been involved in a crash as low sensation seekers. Drivers who reported using

marijuana had 36% greater odds of crash involvement over nonusers. When compared to drivers who slept 8 h or more per night, those who slept less than 8 h were about one-third more likely to have been in a crash. Similarly, those who had a tendency to drive alone drowsy at least sometimes had one-third higher odds of having been in a crash compared to those who rarely or never drive alone drowsy. Gender, race, ethnicity, impulsivity, academic record, and seat belt use as driver or passenger were among those variables not significantly associated with the outcome. The multivariate analysis is summarized in Table 4. Because of the high collinearity (>.80) between age and length of licensure, only length of licensure was included in the model. The analysis controlled for gender, race, ethnicity, neighborhood household income, hours driven per week, urbanicity and region of the country. After stepwise entry of significant variables and removal of non-significant variables, smoking, driving alone while drowsy, and length of licensure were associated with having had a crash as a driver. Non-Hispanic ethnicity also was a significant factor but had a wide confidence interval. Additional checks included adding interactions between smoking and age, average hours slept and driving drowsy alone; average hours slept and age, drowsy driving and age, and drowsy

Table 3 Characteristics of young drivers involved in crashes versus never involved – association with crash involvement (continuous variables)

Age (years) Length licensure (years) Hours driven/week total Hours driven/week with teens Hours sleep/night

Yes (weighted N = 202)

No (weighted N = 303)

Mean

S.D.

Mean

S.D.

p-Value*

19.621 3.369 13.405 5.642 6.895

1.690 1.816 13.465 10.858 1.408

18.892 2.419 11.781 4.990 7.077

1.753 1.746 13.127 8.664 1.423

.000 .000 .174 .456 .150

L. Hutchens et al. / Accident Analysis and Prevention 40 (2008) 869–876 Table 4 Results of multivariate logistic regression modeling of crash involvement Variable

Gender – Male Avg. hours driven/week Ethnicity – non-Hispanic Race – White Race – Black Race – Asian Race – Other Length licensure Household income Region – NE Region – MW Region – S Region – W Residence urban Residence suburban Residence rural Drives drowsy alone Current smoker Constant

OR

1.097 1.209 2.856 – .846 .789 1.111 1.304 1.000 – .631 .826 1.332 – 1.424 1.416 1.798 2.078 .061

95.0% C.I. for OR Lower

Upper

.711 .778 1.040 – .350 .146 .512 1.158 1.000 – .333 .447 .669 – .832 .748 1.079 1.268 –

1.690 1.877 7.839 – 2.047 4.264 2.410 1.469 1.000 – 1.198 1.525 2.649 – 2.436 2.678 2.998 3.404 –

Variables entered into model: gender, avg. hours driven/week, ethnicity, race, length of licensure, household income, region, urbanicity, sensation seeking, hours slept/night, drives drowsy alone, current smoker, current drinker, current marijuana user.

driving and current smoking in the multivariate model. None was significant, and therefore the results are not presented here. 4. Discussion The goal of this analysis was to identify profiles of demographic, behavioral, and driving-related factors that are uniquely related to greater crash odds. We found that 41% of young drivers had experienced a crash as a driver during their lifetimes. Young people who had been licensed longer were more likely to have had a crash, which might be expected due to their likely greater exposure to driving. Despite considerable research demonstrating driving with teen passengers as a risk factor for crashes (Doherty et al., 1998; Simons-Morton et al., 2005), we did not find this association, although our analysis covered a wider license length than the typical 6–12 months of inflated risk. Beyond this, our results confirm that young driver risk involves a confluence of factors, smoking and drowsy driving most prominently, which raises some issues for prevention, messaging, further research, and our current understanding of this driver population. 4.1. Smoking and other substance use Despite the associations we found between a range of health risk behaviors and crash risk in the univariate analysis (including use of alcohol, marijuana, and cigarettes as well as the more general trait of sensation seeking), smoking was the only unique factor among these associated with greater crash odds, independent of demographic factors and general health risk taking, in the multivariate analysis. The relationship between smoking and

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crash involvement was quite robust in our analyses, such that current smokers were more than twice as likely to have had a crash than non-smokers, even after controlling for gender, race, ethnicity, income, length of licensure, and geography. Our analysis builds on a number of all-aged studies (i.e., not youth only) that found a link between smoking and motor vehicle crash risk by extending their applicability specifically to a national sample of teens and young adults. A review of more than 90 studies by Sacks and Nelson (1994) revealed a telling association between smoking and many types of unintentional injury, such that smokers had a 50% increased risk of MVCs, as well as 50% more traffic violations (a difference which persisted after stratifying by age, driving experience, education, and alcohol consumption). Smokers also appeared to have an elevated risk of nighttime MVCs resulting in injury, and the risk of MVCs increased with greater cigarette consumption while driving. The authors recognized the difficulty in separating smoking’s independent contribution to injury risk from the effects of alcohol and other substance use, but they noted alcohol consumption could not entirely account for the association between smoking and crash risk (Sacks and Nelson, 1994). This is also evidenced in our analysis, which controlled for alcohol use. More recent research with all-aged populations also found cigarette smoking to be more common among trauma-involved populations, and more specifically among those involved in MVCs and/or who have sustained injury following an MVC, with increased risk to smokers ranging from 42 to 80% compared to non-smokers (Avi et al., 2001; Wen et al., 2005). Leistikow et al. (2000) concluded that adult smokers in the United States have “significant dose–response excesses of injury death” (p. 277), independent of age, race, gender, alcohol use, seat belt use, education, and marital status. Our research lends support to this conclusion, and extends it to include young driver crash involvement within a nationally representative sample. A few previous studies have also touched on the relationship between smoking and crash risk specifically among youth. Bingham and Shope (2005) found an association between patterns of motor vehicle crashes and adolescent psychosocial and problem behavior characteristics, in which cigarette smoking was included. In a longitudinal study, Shope et al. (2001) determined that reported substance use (cigarettes, marijuana, and alcohol) at age 15 was an important predictor both of increased risk of serious driving offenses and serious crashes as a new driver aged 16–24 years. Recently, Elliott et al. (2006) found cigarette use was positively and significantly associated with traffic incidents – including crash incidence – among young drivers, while a decade earlier, Lang et al. (1996) had shown cigarette use to be a key predictor of single vehicle crashes for females during the first two years of driving. Our study builds on these results by having considered additional personality factors, other risky driving tendencies, and current smoking. Possible explanations given for smoking’s association with increased odds of injury include distractibility involved in the act of smoking (including one-handed steering), direct toxicity (potentially contributing to reduced night vision, vision performance, and reduced driving performance), and associated risk-taking (Sacks and Nelson, 1994; Avi et al., 2001).

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Another plausible rationale is that people who risk their health by engaging in smoking may also be more risk-taking drivers; for example, smokers have been found to wear their safety belts less often (Avi et al., 2001). Smoking was also significantly associated with sensation seeking and alcohol consumption in our study. However, we found that smoking was associated with higher crash odds despite holding constant general risk-taking tendencies as well as other indicators of risky behavior tendencies, such as drinking alcohol, using marijuana, and driving without a seatbelt. Hence, it appears that the risk conferred by smoking is unique to this form of risky behavior. Other potential explanations for the effects of smoking are also unlikely given our pattern of findings. Smokers reported greater sleep impairment, which may be due to smoking comorbidities or to increased anxiety, which has been associated with smoking (Strine et al., 2005). Perhaps more telling, however, was the finding that smokers reported significantly more weekly driving than non-smokers, thereby increasing the probability of crash occurrence. Again, however, we held both sleep and driving amounts constant in our analysis, making it unlikely that they could account for the relation. It is also possible that smoking is a proxy for another factor unexamined here, such as youth rebelliousness, or low parental monitoring. Other studies have linked rebelliousness (Tyc et al., 2004; Choi et al., 2003) and low parental monitoring (Hill et al., 2005; Mott et al., 1999) to greater likelihood of smoking initiation (as well as other health risks) among youth. However, our multiple measures of risky behavior should be a good proxy for these potential influences to the extent they predict engagement in multiple risk behaviors. The tendency to drive while under the influence of alcohol or other drugs was not associated with increased odds of a crash in the multivariate model (although current marijuana use was associated with crashes in univariate analysis). However, very small proportions of respondents reported engaging in these behaviors, suggesting that campaigns of recent years may have been successful in deterring this behavior. Annual crosssectional studies of high school youth have indicated that driving while intoxicated has declined over the past decade (O’Malley and Johnston, 2003; CHOP and State Farm, 2007), so it may be that today’s young drivers engage in this behavior at an insufficient rate to be able to predict crash risk. 4.2. Drowsy driving It is clear that lack of sleep contributes to higher crash odds as confirmed by the univariate association between fewer hours slept and crash involvement in our study. While fatigue is a risk factor for all drivers (Petridou and Moustaki, 2000; Connor et al., 2002), few research studies have focused on fatigue as a teen driver-specific risk issue. Driving drowsy appears to be common among teens: over half reported driving drowsy at least once in the last year, and 15% at least once per week in a 2006 survey by the National Sleep Foundation. As teens get older, this proportion increases: among drivers, 62% of 11th graders and 68% of 12th graders reported driving drowsy within the last year (Carskadon et al., 2006). Young people 16–29 years of age are the most likely to be involved in crashes caused by the driver

falling asleep (Millman, 2005), with one North Carolina study (Pack et al., 1995) finding drivers ≤25 years of age were involved in 55% of all fall-asleep crashes. Groeger (2006) even suggests there may be both “acute” and “chronic” effects of sleep loss, such that sleep-deprived teenagers learning to drive may not be able to effectively master the skills and knowledge required for safe driving. Our finding of an association between crash involvement and driving alone while drowsy is consistent with this research, and its robustness in our model points to drowsy driving as a strong risk factor for crashes. One survey of randomly selected licensed drivers of all ages in New York state also found that 83% of reported crashes due to drowsy driving involved a driver alone in the vehicle (McCartt et al., 1996). The non-significant interaction between hours slept and the tendency to drive alone drowsy in our multivariate model suggests this increased risk arises not just from hours of sleep logged but also behavioral choices about driving in a sleep-deprived state. Driving drowsy while carrying teen passengers was not associated with higher crash odds pointing to a possible safeguard that teen passengers may offer. Simulator data have demonstrated that even low levels of blood alcohol concentrations appear to increase sleepiness as well as reduce perception of crash risk in those with partial sleep deprivation (Banks et al., 2004; Horne et al., 2003). Although not much is known regarding the particular effects of this interaction on young drivers, it is likely that inexperienced drivers may be especially unaware of driving impairment due to sleepiness intensified by alcohol intake. 4.3. Limitations This analysis is limited to determining associations between driver profiling factors and a past crash event. It cannot ascertain whether factors associated with having had a crash are predictive, nor can it determine the association between these factors and crash frequency or severity. Similarly, we know little about the circumstances preceding and surrounding these incidents. Crash involvement can be, depending on circumstances, something of a random event (i.e., being rear-ended at a traffic light), with the possibility of crash involvement increasing with increased exposure – a significant finding in our analyses. The reliance on self-report of crash involvement as an outcome variable is another limitation. Studies have, however, determined self-reported crash incidence among young adults to have high validity (Begg et al., 1999b; Laapotti et al., 2001) and have found proxy driver behaviors, such as speeding, to be reliably correlated with self-reported crash involvement (West et al., 1993). Arthur et al. (2001) even found self-reporting of crashes to be higher than in state records, and concluded that selfreport data are not inherently inferior to archival data. Laapotti et al. (2001) argue deliberate underreporting of crashes is not problematic, so long as analyses concentrate on the difference between driver groups (as our analyses did) rather than absolute number of crashes, and that this underreporting is consistent for all groups studied. Laapotti et al. also found the amount of self-reported driving to be reliable, but the amount of selfreported substance use – particularly prior to driving – was more

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problematic and likely underreported. If these behaviors were underestimated in our research, the associations between crash involvement and substance use/driving after substance use may appear attenuated. Our failure to find an association between higher crash odds and driving with teen passengers may be due to the crudeness of our measure of passenger exposure, particularly the inability to detect occasional (less than 1 h per week) driving with teen passengers. The high prevalence of driving with teen passengers may also have precluded our ability to determine a difference in crash odds. Our study also did not include unlicensed driver crashes, since the survey asked only licensed drivers about their crash involvement. This also may have attenuated our estimates. Additionally, the survey may overrepresent higher risk youth not currently in school. Factors predicting or associated with risky driving and crashes can and do differ. For example, Jonah et al. (2001) found a significant relationship between sensation seeking and risky driving behaviors among college students – including speeding and driving unbelted and after drinking – but not an association with crash involvement. Crash involvement, because of its rare occurrence, may not always be a marker of driver risk, although since randomness should dictate that crash events should be spread evenly among all groups, overall significant associations are still revealing. Also, factors that predict injury crashes among young drivers can be different from those that predict non-injury crashes – a distinction this study cannot make. A key longitudinal study examined a range of demographic, behavioral and personality factors obtained at ages 15 and 18 as potential predictors of outcomes reported at age 21, and determined that there was little agreement between the lifestyle factors that predicted involvement in a traffic crash and those that predicted serious injury (Begg et al., 1999a). 5. Conclusions and implications These findings suggest a significant association between crash involvement and both smoking and drowsy driving, an association rarely explored in the literature. These results suggest an opportunity to address crash risk preemptively in a comprehensive strategy, considering the many factors that influence not only teen driving, but also other processes/events in a teen’s growth and development. Often, prevention efforts around driver risk tend to be directed toward teens at the time they acquire their learner permit or license, in contrast to the much earlier age at which messaging begins around substance use and even sexual activity. Since there does appear to be a relationship between smoking and driving risk, this suggests interventions that begin early and target both issues might be beneficial, particularly since smoking onset often comes at or before the time when young people are also learning to drive. Our results also corroborate previous findings that fatigue augments the risk of a crash – a problem for drivers of all ages, but one that particularly may affect the less experienced. Given the demands of young people’s lifestyles that compete with sleep time, early intervention could also include counseling about the dangers of drowsy driving, as

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well as how to avoid and manage situations where young people may find themselves sleepy and behind the wheel. This may not be accomplished through education alone: anticipatory guidance from physicians could prepare teens to receive information about driving risk; parents should be made aware of contributory risks in order to maximize the transfer of learning during protective learner phase; and communities should be invited to become more engaged in understanding and regulating young driver phases. Smoking and its specific connections with injury risk and motor vehicle crashes are not well understood, particularly involving young people. More work should be done to untangle smoking’s independent contributions to crash risk and determine what else it may represent. We would also benefit from more longitudinal studies examining developmental precursors and driving habits as predictors of risky driving and crash outcomes that include sleep habits and psychosocial factors. This should include studies that can determine causal associations with crashes. Driving, therefore, is a complex behavior, and interventions aimed at helping individuals become safer drivers must consider the complex environments in which this behavior occurs. Knowing more about the context of young driver risk – that is, not just an individual’s profile, but the environment/social context in which that individual’s driving occurs and the choices made around it – can help determine subsequent needs for effective prevention measures. References Arthur Jr., W., Tubre, T., Day, E.A., Sheehan, M.K., Sanchez-Ku, M.L., Paul, D., Paulus, L., Archuleta, K., 2001. Motor vehicle crash involvement and moving violations: convergence of self-report and archival data. Hum. Factors 43, 1–11. Avi, A., Yehonatan, S., Alon, S., Alexandra, H., Arieh, E., 2001. Do accidents happen accidentally? A study of trauma registry and periodical examination database. J. Trauma 50, 20–23. Banks, S., Catcheside, P., Lack, L., Grunstein, R.R., McEvoy, R.D., 2004. Low levels of alcohol impair driving simulator performance and reduce perception of crash risk in partially sleep deprived subjects. Sleep 27, 1063–1067. Begg, D.J., Langley, J.D., Williams, S.M., 1999a. A longitudinal study of lifestyle factors as predictors of injuries and crashes among young adults. Accid. Anal. Prev. 31, 1–11. Begg, D.J., Langley, J.D., Williams, S.M., 1999b. Validity of self reported crashes and injuries in a longitudinal study of young adults. Inj. Prev. 5, 142–144. Bickel, W.K., Marsch, L.A., 2001. Toward a behavioral economic understanding of drug dependence: delay discounting processes. Addiction 96, 73–86. Bina, M., Graziano, F., Bonino, S., 2006. Risky driving and lifestyles in adolescence. Accid. Anal. Prev. 38, 472–481. Bingham, C.R., Shope, J.T., 2005. Adolescent predictors of traffic crash patterns from licensure into early young adulthood. Annu. Proc. Assoc. Adv. Automot. Med. 49, 237–252. Carskadon, M.A., Mindell, J.A., Drake, C., 2006. 2006 Sleep in America Poll: Teens. National Sleep Foundation, Washington, D.C, pp. 1–77. Centers for Disease Control and Prevention, 2003. MMWR Public Health Surveillance for Behavioral Risk Factors in a Changing Environment. Presse Med. 52. Choi, W.S., Harris, K.J., Okuyemi, K., Ahluwalia, J.S., 2003. Predictors of smoking initiation among college-bound high school students. Ann. Behav. Med. 26, 69–74. Connor, J., Norton, R., Ameratunga, S., Robinson, E., Civil, I., Dunn, R., Bailey, J., Jackson, R., 2002. Driver sleepiness and risk of serious

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