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U.S. Adolescent Street Racing and Other Risky Driving Behaviors

  • Indra Neal Kar
    Affiliations
    Health Behavior Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
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  • Chantal Guillaume
    Affiliations
    Health Behavior Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
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  • Kellienne R. Sita
    Affiliations
    Health Behavior Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
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  • Pnina Gershon
    Affiliations
    Health Behavior Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
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  • Bruce G. Simons-Morton
    Correspondence
    Address correspondence to: Bruce G. Simons-Morton, Ed.D., Health Behavior Branch, NICHD, 6710B Rockledge Drive, # 3166, Bethesda, MD 20817. (B.G. Simons-Morton).
    Affiliations
    Health Behavior Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland
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      Abstract

      Purpose

      We examined demographic characteristics and risky driving behaviors associated with street racing among adolescents in the NEXT Generation Health Study (N = 2,395).

      Method

      Binomial logistic regression tested associations between demographics and driving in a street race (DSR) or being a passenger in a street race (PSR). Sequential logistic regression tested the robustness of the association between DSR and crashes.

      Results

      Hispanic/Latino, non-Hispanic Black/African-American, and mixed-race participants were more likely to engage in DSR. Males were more likely and teens with moderate socioeconomic status were less likely to engage in DSR and PSR. DSR was associated with other risky driving behaviors in bivariate models but was not independently associated with crashes after sequential modeling.

      Conclusions

      Among adolescents, those who are male, racial/ethnic minorities, or low socioeconomic status may be at higher risk of DSR. However, overall driving risk might explain the association between DSR engagement and higher crash risk.

      Keywords

      See Related Editorial on p. 509
      Implications and Contribution
      About 13% of a sample of U.S. adolescents reported driving in a street race, which was more likely among males, certain racial/ethnic minorities, and those from families with lower socioeconomic status. Street racers were more likely to engage in other risky driving behaviors.
      Street racing is portrayed as an iconic American pastime, at least in movies, but is a serious international road safety concern. It is mostly considered a planned event in locations with little traffic or where the road is blocked off, often with spectators. It can also occur spontaneously when two drivers pull up beside each other and decide to race [
      • Peak K.J.
      • Glensor R.W.
      Street racing.
      ].
      Despite cross-sectional and retrospective associations with crashes [
      • Li Z.
      • Knight S.
      • Cook L.J.
      • et al.
      Modeling motor vehicle crashes for street racers using zero-inflated models.
      ,
      • Wickens C.M.
      • Smart R.G.
      • Vingilis E.
      • et al.
      Street racing among the Ontario adult population: Prevalence and association with collision risk.
      ,
      • Leal N.L.
      • Watson B.C.
      The road safety implications of illegal street racing and associated risky driving behaviours: An analysis of offences and offenders.
      ], traffic violations [
      • Leal N.L.
      • Watson B.C.
      The road safety implications of illegal street racing and associated risky driving behaviours: An analysis of offences and offenders.
      ], driving while intoxicated [
      • Wickens C.M.
      • Smart R.G.
      • Vingilis E.
      • et al.
      Street racing among the Ontario adult population: Prevalence and association with collision risk.
      ], and risk appraisal [
      • Mirman J.H.
      • Curry A.E.
      Racing with friends: Resistance to peer influence, gist and specific risk beliefs.
      ], street racing has been a largely neglected topic of research [
      • Wickens C.M.
      • Smart R.G.
      • Vingilis E.
      • et al.
      Street racing among the Ontario adult population: Prevalence and association with collision risk.
      ,
      • Vingilis E.
      • Smart R.G.
      Street racing: A neglected research area?.
      ]. Notably, there is a paucity of research on prevalence, predictors, and its covariation with other risky driving behaviors among young, inexperienced drivers.
      In this exploratory study, we assessed demographic characteristics associated with teens driving in a street race (DSR) or being a passenger in a street race (PSR) since not much is known about who is at risk of either activity. We examined cross-sectional associations between street racing and other risky driving measures. We also tested the robustness of the cross-sectional association between DSR and crashes.

      Methods

      Data source

      We analyzed data from Wave 3 (W3; N = 2,395) of the NEXT Generation Health Study, an annual, self-report survey of a nationally representative cohort that started with 10th grade students during the 2009–2010 academic year. In W3, participants (mean age = 18.17 years, SE = .03 years) were surveyed online during the 2011–2012 academic year. The sampling method has been described elsewhere [
      • Hingson R.
      • Zha W.
      • Simons-Morton B.
      • White A.
      Alcohol-induced blackouts as predictors of other drinking related harms among emerging young adults.
      ]. Parental consent and participant assent were obtained at recruitment, and participant consent was obtained after they turned 18 years old. The study was approved by the Institutional Review Board of the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

      Driving measures

      DSR, PSR, and crashes were measured by separate items asking how often each occurred in the past 12 months (0, 1, or 2 or more times). Responses for each measure were dichotomized (any vs. none). The items measuring driving frequency, Checkpoints Risky Driving Scale (C-RDS), and texting/calling while driving asked on how many days in the past 30 days a behavior occurred and were open-response items. Responses greater than 30 were recoded as 30.
      The C-RDS measure had 21 items asking how often certain driving behaviors occurred (e.g., speeding, tailgating, weaving through traffic) [
      • Simons-Morton B.
      • Li K.
      • Ehsani J.
      • Vaca F.E.
      Co-variability in three dimensions of teenage driving risk behavior: Impaired driving, risky and unsafe driving behavior, and secondary task engagement.
      ]. C-RDS captures the overall riskiness of a teen driver, and its reliability and validity were confirmed by objective measures in a naturalistic driving study [
      • Simons-Morton B.
      • Li K.
      • Brooks-Russell A.
      • et al.
      Validity of the C-RDS self-reported risky driving measure.
      ]. We excluded two items: street racing (redundancy with other measure) and driving while intoxicated (DWI), which we analyzed separately. We summed the remaining 19 items. Because of the highly skewed distribution, we dichotomized via median split (higher risk vs. lower risk). DWI was dichotomized such that zero days was no DWI and more than zero days was any DWI.
      Texting/calling while driving was measured with four items asking how often participants did the following: reading a text, sending a text, answering a call, and making a call. We summed the responses. Because of the highly skewed distribution, we dichotomized via median split (more frequently vs. less frequently).

      Demographics

      Participants reported their age, gender, and racial/ethnic background. The parent who provided consent also provided the higher education level of both parents. From the participant report, we estimated family socioeconomic status (SES) using the Family Affluence Scale [
      • Currie C.
      • Molcho M.
      • Boyce W.
      • et al.
      Researching health inequalities in adolescents: The development of the Health Behaviour in School-Aged Children (HBSC) family affluence scale.
      ]. Racial/ethnic background was categorized as non-Hispanic White, Hispanic/Latino, non-Hispanic Black/African-American, non-Hispanic mixed race, or other non-Hispanic minorities. Participants who reported having a driver's license in W1 or W2 and were missing a W3 response were considered licensed. If they reported having a license in W3, they were licensed. If they reported having a permit or no license/permit in W3, they were not licensed.

      Analysis

      All analyses were done in SAS 9.4 and accounted for complex survey design. We first tested bivariate associations between demographics and DSR and between other risky driving behaviors and DSR. Then, demographics associated (p < .05) with DSR were analyzed together in a multivariate model. The same process was done with PSR.
      When examining DSR and crashes, we started with DSR as the only independent variable while controlling for demographics associated with DSR. We added other behaviors as independent variables in the following order: DWI, C-RDS, texting/calling while driving.

      Results

      Table 1 shows distributions of participant characteristics and risky driving behaviors and the binary odds of street racing by demographics. About 13.3% of respondents reported engaging in DSR, and 8.4% reported engaging in PSR. In bivariate models, DSR and PSR were associated with crashes, DWI, C-RDS, and texting/calling while driving. In multivariate analysis, male, Hispanic/Latino, non-Hispanic Black/African-American, and mixed-race participants were more likely than their respective reference groups to engage in DSR; those with moderate affluence were less likely than low affluent participants to engage in DSR. In multivariate analysis, males were more likely to engage in PSR while moderately affluent participants were less likely. The median of C-RDS was 38 (n = 8), whereas the median of texting/calling while driving was 14 (n = 10).
      Table 1Participant demographics, prevalence of DSR and PSR, and prevalence of other risky driving behaviors
      Bivariate regression estimatesMultivariate regression estimates
      Sample statisticsDSR (Ref = no DSR)PSR (Ref = no PSR)DSR (Ref = no DSR)
      Also controlled for frequency of driving in past 30 days.
      PSR (Ref = no PSR)
      CharacteristicsCategoryn%95% CI (%)OR95% CIOR95% CIAOR95% CIAOR95% CI
      Age<18 years old93333.64(27.73, 39.64)(Ref)(Ref)(Ref)(Ref)
      ≥18 years old1,46266.36(60.44, 72.29).94(.53, 1.67).92(.62, 1.35)
      GenderFemale1,33055.26(51.91, 58.61)(Ref)(Ref)(Ref)(Ref)(Ref)(Ref)(Ref)(Ref)
      Male1,06544.74(41.39, 48.09)3.03
      p < .001.
      (1.92, 4.77)1.82
      p < .01;
      (1.17, 2.83)3.39
      p < .001.
      (2.27, 5.08)1.85
      p < .01;
      (1.20, 2.85)
      Race/ethnicityNon-Hispanic White97658.62(46.12, 71.12)(Ref)(Ref)(Ref)(Ref)(Ref)(Ref)
      Hispanic/Latino69719.85(11.79, 27.90)2.94
      p < .001.
      (1.69, 5.11).66(.33, 1.33)1.97
      p < .05;
      (1.07, 3.64)
      Non-Hispanic Black/African-American53814.98(6.80, 23.16)1.77
      p < .05;
      (1.07, 2.93)1.58(.91, 2.75)2.07
      p < .05;
      (1.09, 3.93)
      Non-Hispanic mixed race884.37(2.51, 6.23)3.19
      p < .10;
      (.91, 11.21).75(.19, 2.93)3.62
      p < .05;
      (1.05, 12.47)
      Other non-Hispanic minorities892.18(.94, 3.42)2.93
      p < .10;
      (.89, 9.66).70(.19, 2.61)2.27(.73, 7.04)
      Socioeconomic StatusLow affluence76423.11(16.75, 29.47)(Ref)(Ref)(Ref)(Ref)(Ref)(Ref)(Ref)(Ref)
      Moderate affluence1,12549.00(45.95, 52.05).46
      p < .01;
      (.28, .77).49
      p < .001.
      (.33, .74).60
      p < .05;
      (.38, .95).48
      p < .001.
      (.32, .71)
      High affluence50527.89(22.08, 33.70).51(.20, 1.26).57(.29, 1.13).78(.33, 1.87).59(.30, 1.16)
      Highest parental education levelHigh school diploma/GED or less81631.04(24.78, 37.29)(Ref)(Ref)(Ref)(Ref)(Ref)(Ref)
      Some college education or associate's degree81940.57(36.44, 44.71).52
      p < .05;
      (.28, .97).93(.53, 1.64).64
      p < .10;
      (.37, 1.09)
      Bachelor's degree or more56028.39(21.96, 34.81).47
      p < .05;
      (.22, .98).68(.33, 1.41).52
      p < .10;
      (.27, 1.00)
      Driving licensure statusNot independently licensed1,15933.69(25.42, 41.96)(Ref)(Ref)(Ref)(Ref)
      Independently licensed1,22366.31(58.04, 74.58)1.17(.56, 2.45).99(.62, 1.58)
      Crash involvementNo crashes2,08985.59(82.00, 89.18)(Ref)(Ref)(Ref)(Ref)
      Any crashes29514.41(10.82, 18.00)1.79
      p < .05;
      (1.05, 3.05)2.46
      p < .001.
      (1.66, 3.63)
      DWINo DWI1,37486.95(54.17, 65.57)(Ref)(Ref)(Ref)(Ref)
      Any DWI15913.05(34.43, 45.83)3.81
      p < .001.
      (2.66, 5.44)4.82
      p < .001.
      (3.01, 7.70)
      C-RDS
      Standardized Cronbach's α = .92.
      Lower risk driver1,62357.06(50.74, 63.37)(Ref)(Ref)(Ref)(Ref)
      Higher risk driver76442.94(36.63, 49.26)2.25
      p < .001.
      (1.50, 3.39)2.03
      p < .01;
      (1.29, 3.17)
      Texting/calling while driving
      Standardized Cronbach's α = .93.
      Less frequently1,62357.18(51.27, 63.09)(Ref)(Ref)(Ref)(Ref)
      More frequently76242.82(36.91, 48.73)1.62
      p < .05;
      (1.01, 2.59)2.11
      p < .01;
      (1.27, 3.50)
      DSRNo DSR1,35086.66(84.20, 89.11)(Ref)(Ref)
      Any DSR19413.34(10.89, 15.80)13.47
      p < .001.
      (7.41, 24.49)
      PSRNo PSR2,19691.65(89.83, 93.47)(Ref)(Ref)
      Any PSR1908.35(6.53, 10.17)13.47
      p < .001.
      (7.41, 24.49)
      Values in bold indicate p < .10.
      Binomial logistic regression models testing association of demographics on street racing variables. Descriptive statistics and regression models accounted for complex survey design.
      Multivariate regression models only included demographic variables that had at least one group significantly (p < .05) associated with DSR/PSR in a bivariate model.
      AOR = adjusted odds ratio; CI = confidence interval; C-RDS = Checkpoints Risky Driving Scale; DSR = driving in a street race in the past 12 months; DWI = driving while intoxicated; GED = general equivalency diploma; OR = odds ratio; PSR = being a passenger in a street race in the past 12 months.
      a Also controlled for frequency of driving in past 30 days.
      b Standardized Cronbach's α = .92.
      c Standardized Cronbach's α = .93.
      # p < .10;
      * p < .05;
      ** p < .01;
      *** p < .001.
      Table 2 shows sequential regression models with crashes as the outcome. In Model 1, DSR was positively associated with crashing, but it was not associated after adding DWI in Model 2. In the final adjusted model, only C-RDS and mixed-race identity were positively associated with crashes.
      Table 2Binomial logistic regression models with crashes as the outcome and DSR, DWI, C-RDS, and texting/calling while driving as independent variables added in sequence
      Crash involvement (Ref = no crashes)
      Model 1 (n = 1,411)Model 2 (n = 1,400)Model 3 (n = 1,394)Model 4 (n = 1,386)
      AOR95% CIAOR95% CIAOR95% CIAOR95% CI
      GenderFemale (Ref)
      Male.84(.52, 1.35).83(.53, 1.30).86(.54, 1.36).86(.55, 1.36)
      Race/ethnicityNon-Hispanic White (Ref)
      Hispanic/Latino.74(.34, 1.60).72(.32, 1.62).77(.34, 1.75).75(.32, 1.73)
      Non-Hispanic Black/African-American.89(.48, 1.67).90(.49, 1.66).92(.49, 1.71).92(.49, 1.71)
      Non-Hispanic mixed race2.89
      p < .01;
      (1.42, 5.86)2.99
      p < .001.
      (1.61, 5.57)2.95
      p < .01;
      (1.45, 5.99)2.89
      p < .01;
      (1.45, 5.77)
      Other non-Hispanic minorities1.16(.29, 4.69)1.14(.31, 4.25)1.16(.30, 4.43)1.13(.30, 4.26)
      Socioeconomic StatusLow affluence (Ref)
      Moderate affluence1.37(.70, 2.68)1.32(.68, 2.58)1.22(.64, 2.31)1.19(.62, 2.27)
      High affluence1.52(.78, 2.97)1.50(.76, 2.94)1.43(.71, 2.89)1.36(.66, 2.78)
      Highest parental education levelHigh school diploma/GED or less (Ref)
      Some college education or associate's degree.95(.55, 1.65).93(.53, 1.61).93(.52, 1.65).91(.51, 1.62)
      Bachelor's degree or more.76(.37, 1.55).70(.34, 1.44).71(.34, 1.46).70(.34, 1.46)
      Frequency of driving1.02
      p < .10;
      (1.00, 1.05)1.02(.99, 1.05)1.00(.97, 1.03)1.00(.97, 1.03)
      DSRNo DSR (Ref)
      Any DSR1.74
      p < .05;
      (1.06, 2.85)1.52(.80, 2.87)1.35(.68, 2.65)1.34(.70, 2.59)
      DWINo DWI (Ref)
      Any DWI2.06
      p < .10;
      (1.00, 4.25)1.74(.83, 3.68)1.65(.81, 3.40)
      C-RDSLower risk driver (Ref)
      Higher risk driver2.10
      p < .01;
      (1.25, 3.55)1.86
      p < .05;
      (1.14, 3.04)
      Texting/calling while drivingLess frequently (Ref)
      More frequently1.36(.86, 2.15)
      χ285.77
      p < .001.
      85.56
      p < .001.
      120.64
      p < .001.
      150.28
      p < .001.
      df11121314
      Values in bold indicate p < .10.
      AOR = adjusted odds ratio; CI = confidence interval; C-RDS = Checkpoints Risky Driving Scale; df = degrees of freedom; DSR = driving in a street race in the past 12 months; DWI = driving while intoxicated; GED = general equivalency diploma; χ2 = Wald chi-square statistic.
      Each model accounted for complex survey design.
      The frequency of driving was the number of days driving in the past 30 days. Sample size values (n) refer to the total sample size analyzed by the models.
      # p < .10;
      * p < .05;
      ** p < .01;
      *** p < .001.

      Discussion

      Although prevalence of PSR (8.4%) and DSR (13.3%) was modest, males and those with low SES had elevated odds of PSR, whereas DSR was more likely among males, racial/ethnic minorities, and those from low SES families. Consistent with past studies [
      • Li Z.
      • Knight S.
      • Cook L.J.
      • et al.
      Modeling motor vehicle crashes for street racers using zero-inflated models.
      ,
      • Wickens C.M.
      • Smart R.G.
      • Vingilis E.
      • et al.
      Street racing among the Ontario adult population: Prevalence and association with collision risk.
      ,
      • Leal N.L.
      • Watson B.C.
      The road safety implications of illegal street racing and associated risky driving behaviours: An analysis of offences and offenders.
      ], DSR was associated with other risky driving behaviors. However, in sequential modeling, C-RDS, not DSR, was associated with crashes. Therefore, overall driving risk might have explained the cross-sectional association between DSR and crash risk, indicating DSR may be just one of several related measures of driving risk, as suggested in a study on self-appraisal of risk [
      • Mirman J.H.
      • Curry A.E.
      Racing with friends: Resistance to peer influence, gist and specific risk beliefs.
      ]. Prospective studies might further clarify this and the possible roles of SES and racial/ethnic identity on teen street racing.

      Acknowledgments

      This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Heart, Lung, and Blood Institute , the National Institute on Alcohol Abuse and Alcoholism , and Maternal and Child Health Bureau of the Health Resources and Services Administration, with supplemental support from the National Institute on Drug Abuse (Contract # HHSN275201200001I ).

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      Linked Article

      • Agency, Adolescence, and Motor Vehicle Crash Risk
        Journal of Adolescent HealthVol. 62Issue 5
        • Preview
          Motor vehicle crashes (MVCs) remain a leading cause of death and disability for adolescents worldwide [1,2]. Population-level crash data indicate that age and experience interact to influence crash involvement; crash rates peak at licensure and are highest initially for the youngest drivers [3–5]. The most noticeable reductions occur during the initial 6–9 months of licensure [4,5]. Based on these patterns, research on young drivers has focused in two areas: developmental factors salient to adolescence and practical inexperience.
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