Crash Injury Risk Behavior in Adolescent Latino ...

Vol 55 • October 2011

Crash Injury Risk Behavior in Adolescent Latino Males: The Power of Friends and Relational Connections Federico E. Vaca, MD, MPH

Yale University School of Medicine Department of Emergency Medicine

Craig L. Anderson, MPH, PhD

University of California, Irvine School of Medicine Center for Trauma and Injury Prevention Research ________________________________

ABSTRACT – The adolescent Latino male mortality profile is an anomaly when compared to an otherwise more favorable overall U.S. Latino population mortality profile. Motor vehicle crash fatalities bear a considerable proportion of mortality burden in this vulnerable population. Friend influence and relational connection are two contextual domains that may mediate crash injury risk behavior in these adolescents. Our study goal was to assess the role of friend influence over time and relational connections associated with crash injury risk behavior (CIRB) in adolescent Latino males. Waves I and II data from the National Longitudinal Study of Adolescent Health were used. Scale of CIRB, and three relational connections; school connectedness, parent connectedness, and expectation of academic success were developed and tested. Friend nomination data were available and the index student responses were linked to friend responses. Linear regression was used to assess the relationship of relational connections and friend CIRB on index student CIRB at wave I and II. Longitudinal analysis did not show significant evidence for friend influence among adolescent Latino males on CIRB. The best predictor of CIRB at wave II for adolescent Latino males was their CIRB at wave I. Relational connections were important yet exaggerated cross-sectionally but their effect was substantially attenuated longitudinally. The lack of friend influence on CIRB for adolescent Latino males may be specific to this demographic group or characteristic of the sample studied. Prevention strategies that focus on modulating friend influence in adolescent Latino males may not yield the desired prevention effects on CIRB. __________________________________

INTRODUCTION From the year 2000 to 2010, the U.S. Latino population grew by 43% as compared to about 5% of the non-Latino population to consist of more than 50 million [Humes and Jones et al. 2011]. This growth accounted for more than half of the entire U.S. population growth in that same time frame. Latinos now make up 16% of the nation’s population which is expected to triple by 2050. Because of the ongoing Latino population growth along with the interest of public health researchers to more thoroughly understand socioeconomic and cultural determinants of health in this vulnerable group, U.S. Latinos remain a focus of research interest in health disparities. From a historical perspective, a notable example of this interest points to a landmark study by Markides and Coreil [1986] and their description of the Latino Epidemiologic Paradox (LEP, also known as the Hispanic Paradox). The paradox describes overall favorable mortality profiles for U.S. Latinos compared to non-Latino Whites despite poverty, low education, and low access to healthcare. Examination and analysis of key health indicators of U.S. Latinos such as life expectancy, infant mortality, mortality from cardiovascular diseases, and measures of functional health by Markides and Coreil support their claim of the LEP. C ORRESPONDING AUTHOR: Federico E. Vaca, MD, MPH, Department of Emergency Medicine, Yale University School of Medicine, 464 Congress Avenue Suite 260, New Haven, CT, 06519, USA Email: [email protected]

Subsequent to Markides and Coreil’s work, HayesBautista et al. identified the LEP and an anomalous increased mortality of Latino adolescent and young adult males in California mortality data (1989 - 1997) [Hayes-Bautista and Hsu et al. 2002]. This anomaly was labeled the Latino Adolescent Male Mortality Peak (LAMMP) and showed that motor vehicle crash fatalities constituted a significant proportion of the mortality burden in this anomaly. For over fifteen years, the National Highway Traffic Safety Administration has been warning of impending growth in the disproportionate contribution of crash injuries and fatalities by the overall younger U.S. Latino population [NHTSA 1995]. Despite recent notable reductions in overall motor vehicle crash fatalities in the United States [NCSA 2008, 2010], traffic collisions remains a formidable public health threat to U.S. adolescents. Topping the list of causes of death for youth, the burden of crash injury morbidity and mortality remains unacceptably high. Moreover, recent studies show unfavorable and disparate trends in the areas of gender and ethnicity in teen crash-related morbidity and mortality. [Tsai and Anderson et al. 2008; Vaca and Anderson 2009; Tsai and Anderson et al. 2010; Peek-Asa and Yang et al. 2011]. For Latino adolescent and young adult males, from 1999 to 2005, motor vehicle crash mortality rates have increased, and in 2005 exceeded

55 AAAM Annual Conference Annals of Advances in Automotive Medicine October 3‐5, 2011 th

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©Annals of Advances in Automotive Medicine

the rates for that of non-Latino White males [Vaca and Anderson 2009]. Recent study results of adolescents show the behavioral differences that may contribute to the risk of crash injury. More young males than females rarely or never wear a seat belt [CDC 2010] and drive when drinking [CDC 2010; Tsai and Anderson et al. 2010], although the latter difference has narrowed in recent years [Tsai and Anderson et al. 2010]. Young males drive faster and with shorter headways than older drivers and this difference becomes more pronounced in the presence of a male passenger [McKenna and Waylen et al. 1998; Simons-Morton and Lerner et al. 2005]. Latino adolescents report more often than non-Latino White adolescents that they had ridden with a driver who had been drinking alcohol [CDC 2010].

Williams et al. found that the presence of teen passengers increases the risk of a crash for teen drivers [Williams and Ferguson et al. 2007]. This effect has been described as peer influence, although it may also include driver distraction by passengers that does not depend on any affinity between the driver and passenger. On the other hand, friend influence may be conceptualized as the adoption of friends’ attitudes and behavior, so that the behavior of pairs of friends would become more alike over time. This influence may be moderated by parental influence and other social connections, so that adolescents with weaker connections to school and parents may be more influenced by friends. At least one longitudinal study by Bingham and Shope [2004] showed that teens with weaker parental and social bonds, in part, characterize the developmental trajectory of young adult risky drivers.

While there is an appropriate focus of many researchers on risky behavior in teen drivers [Shope and Bingham 2008] and passengers [Preusser and Ferguson et al. 1998; Williams and Ferguson et al. 2007], more needs to be known about contextual factors and youth relational connections that may mediate and or moderate behaviors that influence their risk of crash injury. This type of information could identify previously less considered or new contextual constructs that could be integrated into the development and testing of prevention and intervention models; particularly for some of the most vulnerable youth groups as in the case of young Latino males [Hayes-Bautista and Hsu et al. 2002; Vaca and Anderson 2009; Vaca and Anderson et al. 2010].

However, the extent to which friend influence and relational connections has been studied in Latino youth traffic safety is limited. A notable knowledge gap remains in this area. Taking the LEP, LAMMP and the overall frequency of adolescent morbidity and mortality due to crash injury into account, we believe an important opportunity exists to longitudinally study friend influence and contextual factors associated with behaviors that influence the risk of crash injury in this vulnerable group. With the nation’s public health focus in eliminating health disparities, this is of particular importance. The goal of our study was to assess the role of friend influence over time and relational connections associated with crash injury risk behavior (CIRB) in adolescent Latino males.

The influence of friendships and relational connections on adolescents have been well studied in several adolescent risky behaviors [Resnick and Bearman et al. 1997; Stevens-Watkins and Rostosky 2010]. In particular, the power of friends is known to be a notable source of influence in cross-sectional studies of adolescent alcohol use and sexual behavior. Similarly, correlational and longitudinal studies have shown parent and school connectedness (relational connections) to impart some level of resilience and or protection in youth as they traverse the adolescent life period [Resnick and Bearman et al. 1997; Scal and Ireland et al. 2003; Cavanagh 2008; Franko and Thompson et al. 2008]. These cross-sectional relationships include both the selection of friends and the influence of friend behavior on subsequent behavior. Longitudinal data are essential to distinguish between selection and an influence of friend behavior on changes on behavior.

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METHODS Data from waves I and II of the National Longitudinal Study of Adolescent Health (Add Health) were analyzed. The Add Health study is a longitudinal in-home and in-school interview study of a nationally representative sample of students (with over sampling of minority youth) in grades 7 to 12 from 132 schools in the United States during the 1994 – 1995 school year [UNC Carolina Population Center 2004]. This cohort has been followed into young adulthood with interviews occurring as recent as 2008 when the sample was age 24 – 32 years old. Interview waves approximately 1, 2, 6 and 13 years after initial recruitment followed changes in adolescent behavior over time. The surveys include questions that characterize adolescents’ relationships to their parents, friends, peers, schools, and communities; emotional well-being; substance use;


romantic and development.

sexual

behavior;

and

physical

In the in-home interview, the Add Health study participants were asked to list their five closest male and five closest female friends. The study identification number of the friend was included in the data file if the friend was not a romantic partner of the student and could be identified among the students interviewed at the same school or a sister school (that is, at a different grade level serving the same community). Romantic partners, friends at other schools, and friends at the same school or sister school who were not on the list of interviewed students were identified by category but not by study identification number. At sixteen schools all students were interviewed (the saturation sample) so there was a high probability that data were collected from the friends listed by the index student (student of primary interest). Latino male students at the saturation schools were linked to the closest male and female friend for which interview data was available. Measures Crash injury risk behavior (CIRB). Add Health includes a number of survey questions related to adolescent crash injury risk behavior. We included the following variables: motorcycle riding (five responses, never to almost every day), wearing a seatbelt (five responses, never to always), driving after drinking in the past 30 days (five responses, never to six or more times), ever driven drunk (binary), ever driven while high on drugs (binary), driving a car without the owner’s permissions (four responses, never to five or more times), having a driver license (binary), weekly miles driven (four responses, none to over 100 miles), usual drinks per occasion (number, truncated at 12), frequency of alcohol use ), frequency of having five or more drinks on an occasion, and frequency of getting drunk. The latter three variables each had seven responses, (every day or almost every day to never). We excluded four similar items that had missing values for more than 30% of subjects: motorcycle helmet use, bicycle helmet use, racing (bike, skateboard, rollerblades, boat or car), and doing something dangerous because of a dare. These questions were asked at both wave I and wave II, and we calculated CIRB for each wave.

Add Health recorded values of up to 90 drinks per occasion. We transformed 686 reports of 13 to 90 drinks (3.3%) to 12 drinks or more because we believe that this number represents a very substantial risk for adolescents. We reversed the coding for the frequency of alcohol use, frequency of having five or more drinks on an occasion, and frequency of getting drunk, so that 0 represented never and 6 represented every day or almost every day, which we found more intuitive. For several questions, we assigned a response of never when that answer was implied from a previous branching question. Driving at least weekly without a license was calculated from the driver license variable and weekly miles driven. This variable reflects both driving with a learner’s permit and unlicensed driving. Connection to School. We assessed five variables as measures of connection to school. These were feeling close to people at your school, feeling like you are a part of your school, happy to be at your school, teachers at your school treat students fairly, and feeling safe in your school. Each question was answered on a five-point scale, from 1 (strongly agree) to 5 (strongly disagree). We reversed the score of these items so that a high score indicated high connectedness. Connection to Parents Identification of Parents. The first step in assessing connections to parents is to identify parents or individuals in households of adolescents who assume parental roles. The Add Health household file included a roster of household members with a variable for each individual describing their relationship to the respondent. If none of the relationships included a parental figure, the adolescent was asked who in the household acts in the place of a mother and a father to him or her. We used both the roster and this question to identify the maternal and paternal figures in the household and their relationship to the respondent. Questions about parents were asked in reference to parental figures in their household, and not in reference to biological parents who lived elsewhere. Six questions, each on a five point scale, were used to assess connection to the maternal figure in the household: how close do you feel to your mother; how much do you think she cares about you (both items on a scale of 1 to 5, not close at all to extremely close); is your mother warm and loving to you; when you do something wrong, your mother helps you

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understand why it is wrong; satisfaction with the way your mother and you communicate; and overall satisfaction with your relationship with your mother. Each of the four latter items, with five responses from 1 (strongly agree) to 5 (strongly disagree), was reversed, so that higher scores indicated greater closeness for every question. The same questions, with the exception of the fourth (when you do something wrong), were also asked with respect to the father figure in the household. Expectation of Academic Success. The responses to the following questions were used to measure expectation of academic success: how much do you want to go to college, how likely is it that you will go to college, how disappointed would (your mother) be if you did not graduate from college and how disappointed would (your father) be if you did not graduate from college. Each questions was answered on a scale from 1 (low) to 5 (high). This study was exempted from review by the University of California, Irvine, Human Subjects Research Institutional Review Board. Data Analysis The data were analyzed using IBM SPSS Statistics (version 18.0, IBM, Somers, NY). In order to construct the CIRB scale, connection to school and parent measures, and expectation of academic success, respective responses to survey questions from the entire Add Health sample of wave I were used (n= 20,745). Scale reliability was assessed using Cronbach’s α. The regression analysis assessing the relationship of connectedness and friend CIRB on index student CIRB was restricted to Latino male students age 15 – 19 years at the saturation schools. Because connectedness and CIRB were measured on unequal arbitrary scales, standardized regression coefficients were used to assess the relationship between these scales. For bivariate models the correlation coefficient, r, is equal to the standardized regression coefficient. For the multivariate models, R2 is reported. Confidence intervals were calculated from the standardized regression coefficients and t-scores in the SPSS output. We first report the distribution and reliability of each of the measure described. Then we examine the cross-sectional relationship of connectedness and friend CIRB, measured at wave I, on wave I CIRB in the index subject. Finally, we examine the

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longitudinal effect of these wave I variables on wave II CIRB in the index subject. RESULTS Crash Injury Risk Behavior The reliability among the 13 items for CIRB was good (α =0.74). The CIRB score was calculated as the mean of the 13 items, each scaled to a range of 0 to 1, and then transformed using the formula 1.0 plus the log of the risk score plus 0.1. Fifteen percent of the Add Health sample had a score of 0, corresponding to the least risky response for every question. In this sample, including males and females, the CIRB score had a range of 0.00 to 0.94, a mean of 0.32, and a standard deviation of 0.22. Connection to School The five variables used as measure of connection to school all had means between 3.4 and 3.7, indicating, on average, a somewhat positive connection to school. There were no missing values. The reliability was very good (α =0.87). There were no items for which deletion would have improved alpha. The mean score was 3.59 and the standard deviation was 1.00. Connection to Parents Identification of Parents. Eighty-four percent of adolescents lived with their biological mothers, less than 56% with their biological fathers, and 50% lived with both biological parents. More adolescents lived with a stepfather (8.0%) than a stepmother (2.2%). About 2.5% of mothers and fathers were adoptive. For 1.9%, both parents were adoptive. Thirty percent of respondents had no paternal figure and 6% had no maternal figure in the household. Eighty-eight (0.4%) identified the same person as acting as a mother and a father to the respondent. This individual was counted in both the maternal and paternal responses. All six questions used to assess connection to mother were known for 19,404 respondents (99.6% of those living with a maternal figure). The item means ranged from 4.0 to 4.8, indicating, on average, a strong connection to mother. There was very good reliability (α=0.85). Deletion of one item, how much do you think she cares about you, would have increased the alpha by less than .01, and the item was retained. The mean of the six items was 4.36 and the standard deviation was 0.64. For permanent mothers (biological, adoptive and step/adoptive), the overall


mean=4.37 and α=.85; for step mothers, the mean=3.85 and α=0.90; and for other maternal figures, the mean4.32 and α=0.84. Thus, agreement was very good among all three types of maternal figures. All five questions used to assess connection to father were known for 14,403 respondents (99.6% of those living with a paternal figure). The item means ranged from 3.9 to 4.7, indicating, on average, a strong connection to father. There was very good reliability (α=0.88). Deletion of one item, how much do you think he cares about you, would have increased the alpha by less than .01, and the item was retained. The mean of the five items was 4.22 and the standard deviation was 0.77. For permanent fathers (biological, adoptive and step/adoptive), the overall mean=4.27 and α=0.88; for step fathers, the mean=3.85 and α=0.90; and for other paternal figures, the mean=4.22 and α=0.85. Agreement was very good among all three types of paternal figures. Expectation of Academic Success The four items, that is desire and expectation of going to college, and mother and father’s disappointment if the respondent did not graduate from college, has good agreement (α=0.735) in the 13,539 respondents (65%) who had values for all four variables. When we substituted the mean of the latter two items (mother’s and father’s disappointment if the respondent did not graduate from college) for the separate items, 20,112 respondents (96.9%) had all the items, and α=0.723. The three items had means from 4.0 to 4.4. The mean of the three items was 4.18 and the standard deviation was 0.91 Adolescent Latino Males The CIRB scale and measures of connections to school and parents, and expectations of academic success were applied to the primary sample of interest of adolescent Latino males age 15 – 19 years. The Add Health data included 354 Latino male students at the 16 schools in the saturation sample. We excluded 110 subjects who had no CIRB data for wave II. This made it impractical to study change over time for these subjects. In addition, 7 students with no school connectedness data and 3 students with no data for parent connectedness were excluded, leaving 234 students for primary analysis of interest. The results of linkage students to their closest shown in Table 1. students were linked to

of these 234 Latino male male and female friends are One hundred-seventy-three a male friend and 116 were

linked to a female friend. The first listed male friend was linked for 111 students (64% of male friends linked), and the first listed female friend was linked for 62 students (53% of female friends linked). Table 1. Rank of closest friend with interview data or type of first friend response, adolescent Latino male students in Add Health saturation sample with complete data on connectedness and CIRB, n=234. Rank of closest Male Female friend with friends friends interview data number (%) number (%) 1 111 (47%) 62 (26%) 2 37 (16%) 37 (16%) 3-5 25 (11%) 17 (7%) If no friend with interview data, type of first friend listed Friend at 32 (14%) 48 (21%) another school Friend at same 15 (6%) 14 (6%) school with no interview data Listed no friends 14 (6%) 48 (21%) Romantic partner 0 (0%) 8 (3%) Total 234 (100%) 234 (100%) The characteristics of the sample are shown in Table 2. The mean age was 17 years, and 72% of the adolescents were age 16 or 17. The school connectedness and academic expectations were higher than the neutral score (3) and parent connectedness was closer to the maximum score (5). Table 2. Characteristics of adolescent Latino male students in Add Health saturation sample. Characteristics n range mean st. dev. Age 234 15-19 17.0 0.9 School 234 1.4-5.0 3.5 0.7 connectedness Parent 234 1.8-5.0 4.3 0.6 connectedness Academic 234 1.0-5.0 3.8 0.9 expectations CIRB, Wave I 234 .00-.87 .40 .23 CIRB, Wave II 234 .00-.91 .46 .21 Closest male 173 .00-.84 .40 .21 friend CIRB, Wave I Closest female 116 .00-.84 .31 .20 friend CIRB, Wave I

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Table 3 shows bivariate and multivariate standardized regression coefficients for the linear regression of index student CIRB on connectedness in three domains and friend CIRB. These coefficients represent the cross-sectional relationship found in the wave I data. For the bivariate coefficients, the number of subjects for each variable is the same as that given in Table 2. For the multivariate regression, n=173. In the bivariate regression, each of the three measures of connectedness, and male friend CIRB, but not female friend CIRB, are related to index student CIRB. In the multivariate model shown (r=0.41), each of these associations is attenuated, but school connectedness, academic expectations, and male friend CIRB are still related to index student CIRB. In a multivariate regression using all five variables to predict index student CIRB among the 103 students with data for both male and female friends (not shown), female friend CIRB had standardized regression coefficient of 0.04 (95%CI 0.15 to 0.23) and the other variables had standardized regression coefficients similar to those in a regression model without female friend CIRB using the same 103 students. Table 3. Standardized regression coefficients (and 95% confidence intervals) for index student CIRB at wave I on connectedness in three domains and friend CIRB, adolescent Latino male students in Add Health saturation sample, n=173, except as noted. Bivariate Multivariate R2 0.17 School -0.29 -0.18 connectedness (-0.43 to -0.14) (-0.33 to -0.03) Parent -0.20 -0.10 connectedness (-0.35 to -0.06) (-0.25 to 0.06) Academic -0.25 -0.15 expectations (-0.39 to -0.10) (-0.30 to -0.01) Closet male 0.27 0.22 friend CIRB (0.13 to 0.42) (0.08 to 0.36) Closest female 0.15 friend CIRB* (-0.03 to 0.33) *n=116 In bivariate regression (not shown), school connectedness was negatively associated with closest male friend CIRB and parent connectedness was negatively associated with closest female friend CIRB. Table 4 shows multivariate standardized regression coefficients for connectedness and friend CIRB. Only school connectedness was negatively associated with closest male friend CIRB, and none of the connectedness variables were associated with female friend CIRB.

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Table 4. Multivariate standardized regression coefficients (and 95% confidence intervals) for friend CIRN on connectedness in three domains adolescent Latino male students in Add Health saturation sample. Closest male Closest female friend CIRB friend CIRB (n=173) (n=116) R2 0.04 0.07 School -0.18 -0.11 connectedness (-0.34 to -0.02) (-0.31 to 0.08) Parent 0.09 -0.16 connectedness (-0.07 to 0.25) (-0.36 to 0.04) Academic -0.10 -0.06 expectations (-0.26 to 0.05) (-0.26 to 0.13) Table 5 shows bivariate and multivariate standardized regression coefficients for the longitudinal relationship between connectedness and friend CIRB, and index student CIRB at wave I on index CIRB at wave II. For the bivariate coefficients number of subjects for each variable is the same as that given in Table 2. Each of the three measures of connectedness and the two values for friend CIRB had similar bivariate standardized regression coefficients for index student CIRB at wave II as they had for index CIRB at wave I. However, index CIRB at wave I had a much larger standardized regression coefficients for index student CIRB at wave II than any of the other variables. In the multivariate model shown (r=0.63), each of these associations are substantially attenuated, except that index student CIRB at wave I still had a strong association with index student CIRB at wave II. In a multivariate regression using all six variables to predict index student CIRB among the 103 students with data for both male and female friends (not shown), female friend CIRB had standardized regression coefficient of -0.08 (95%CI -0.23 to 0.07) and the other variables had standardized regression coefficients similar to those in a regression model without female friend CIRB using the same 103 students.


Table 5. Standardized regression coefficients (and 95% confidence intervals) for index CIRB at wave II on connectedness in three domains and friend CIRB at wave I, adolescent Latino male students in Add Health saturation sample, n=173 except as noted. Bivariate Multivariate R2 0.40 School -0.19 0.00 connectedness (-0.34 to -0.05) (-0.13 to 0.14) Parent -0.18 -0.07 connectedness (-0.33 to -0.03) (-0.20 to 0.06) Academic -0.14 0.03 expectations (-0.29 to 0.01) (-0.09 to 0.16) Index student 0.63 0.61 CIRB (0.51 to 0.74) (0.48 to 0.74) Wave I Closet male 0.20 0.04 friend CIRB (0.05 to 0.34) (-0.09 to 0.16) Wave I Closest female 0.13 friend CIRB (-0.05 to 0.31) Wave I* *n=116

expectations had a lower CIRB. However, when all the contextual items were considered, it was the CIRB of the closest male friend that had the strongest relationship to the adolescent Latino male’s CIRB at wave I. The similarity of crash injury risk behavior between friends at this point was more important than the other measured relational connections.

DISCUSSION

Jaccard used the Add Health saturation sample to study friend influence on sexual activity and binge drinking [Jaccard and Blanton et al. 2005]. He used data from both sexes and all race/ethnic groups. For both behaviors of interest, friend influence had a small but reliable effect in longitudinal analysis.

Contextual factors such as the influence of friends, connectedness to school and parents as well as youth expectations of academic success have been well studied in regards to their affect on adolescent risky health behavior [Resnick and Bearman et al. 1997; Maxwell 2002]. Published studies exploring similar contextual factors when evaluating crash injury risk behaviors in adolescents are lacking. Given the national priority and importance of addressing injury disparities coupled with recent unfavorable crash injury mortality trends and the LAMMP [Vaca and Anderson 2009; Vaca and Anderson et al. 2010], we sought to explore the extent of influence that friends and relational connections have on these particularly vulnerable youth. The development of the CIRB scale provided us with a reasonable method of assessing the adolescent’s level of self-reported behavior that could lead to greater risk of crash injury. Given this statistic and its good reliability, we were able to explore CIRB and its relationship to contextual connections (i.e. friends, school, parents) as well its change over sequential interview waves in the adolescent Latino male. In the cross-sectional portion of our study, we found that Latino adolescent males with stronger connections to school, parents, and academic

When we considered the CIRB of the adolescent Latino male closest friend in relation to the connectedness measures, only school connectedness showed a relationship with the closest male friend CIRB. The longitudinal analysis provides the best modality of measuring this friend influence. Assessing CIRB over time, the best predictor of index student CIRB at wave II was the index CIRB at wave I. Similarly, when all the relational connection measures were considered together in the multivariate regression, only the index student CIRB at wave I predicted the CIRB at wave II. Thus, the relationships found in the cross-sectional data did not have a continuing influence on the index student's CIRB over time.

Our study findings differ from Jaccard in that we found no measurable effect of friend influence or relational connection on CIRB over time. It should be recognized that Jaccard did not limit his study population to Latino adolescent males as we did in our study. We believe our findings could be mediated by a cultural context that our study population is faced with and which we did not measure. Thus, traffic safety interventions based solely on social relationships may have limited effects on crash injury risk behavior of Latino adolescent males. STRENGTHS AND LIIMITATIONS First of all, we believe that the strength of this study lies in the overall rich and contextually informative data that exist within the National Longitudinal Study of Adolescent Health. This data set provides a comprehensive and intentional longitudinal focus on adolescent health. Other studies have used the Add Health data to study contextual factors as they relate to the well being of

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adolescents. Similarly these studies developed and tested scales that reliably measured adolescent connection to school [Johnson and Crosnoe et al. 2001; Johnson and Crosnoe et al. 2006; Vaquera and Kao 2008; Bui 2009], connection to parents [Johnson and Crosnoe et al. 2006; Stokes 2008], and expectations of academic success [Stokes 2008]. We realize that our study is not without limitations. First of all, the number of study subjects available for our longitudinal analysis was limited because a number of subjects did not complete wave II. Our focus was on Latino adolescent males, and the effect friend influence may be different in other groups. Moreover, while the National Longitudinal Study of Adolescent Health remains an active study, the wave I cohort dates back to high school students during the 1994-1995 school year. It is probable that both demographic and secular changes have influenced the CIRB of adolescents since that time. Further, the whole of the Add Health study was not specifically developed and implemented with traffic safety measures or injury prevention in mind. As a result, we are limited by the number and specificity of the traffic safety-related questions that are available to us to utilize in order to construct our crash injury risk behavior scale. As a result, within the scope of this data set, we are limited in the extent to which we can study this crash injury risk behavior and unfortunately some issues of notable importance are by default left under studied. For example, recent research has noted an increase in adolescents regularly participating in unlicensed driving [Hanna, Taylor et al. 2006; Elliott and Ginsburg et al. 2008; Garcia-Espana and Ginsburg et al. 2009]. While the adolescents interviewed in the Add Health study were not directly asked if they had ever illegally driven without a license, we did attempt to address this question by using two driving-related questions to calculate an answer (Do you have a valid driver’s license?, About how many miles do you driver each week?). However, because of the lack of specificity in questioning, we understand that in some cases “unlicensed driving” can be legal (driving with an instruction permit). Despite the lack of specificity of traffic safety questioning in Add Health, this unique longitudinal adolescent cohort data maintains a wealth of information that can still allow for the meaningful evaluation of important self reported behaviors that are known to influence the risk of crash injury. Several of the traffic safety questions incorporated in the Add Health interview are similarly used in other national youth-health and well being surveys like the

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Youth Risk Behavior Survey (YRBS) [CDC 2010]. Further because of the friend-nomination and friendinterview data component contained within the Add Health study, we were able to evaluate friend influence on risk behavior that deals with important adolescent traffic safety issues. This is an area of research that remains understudied and unpublished in the injury prevention literature. Finally, while the whole Add Health weighted adolescent sample estimates can be applied to the U.S. high-school student population (for the time period of wave I), our main sample of interest (adolescent Latino males) for this study used the saturation sample of Add Health. As a result, this sample could not be weighted to accurately reflect a national representative sample. In addition, the Add Health sample excludes adolescents not in high school. It should be noted that responses of crash injury risk behavior among high-school dropouts may be quite different than those for high-school students studied in the wave I cohort [CDC 1994]. CONCLUSION Our cross-sectional analysis confirms the similarity of friends’ crash injury risk behavior in Latino adolescent males. However, the longitudinal analysis did not show any continuing friend influence. Studies of other risky behaviors in adolescents have shown a modest friend influence effect. We believe that among Latino adolescent males, friend influence of crash injury risk behavior may be mediated by cultural factors. Future research should examine friend influence within a Latino cultural context to disentangle such a causal relationship. Without this understanding, traffic injury prevention strategies in Latino adolescent males that focus on modulating friend influence may not yield the desired prevention effects on CIRB. ACKNOWLEDGMENTS The project described was supported by Grant Number K23HD050630 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.


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