(p. 40) Risk-Taking Behaviors across the Transition from Adolescence to Young Adulthood
The transition from adolescence to adulthood (ages 18–25) can be defined as a period of exploration of identity and one’s place in the world. Young adults achieve new levels of independence from their parents, adopt considerably greater responsibilities, and often make important life decisions on their own for the first time in their lives. While this is an exciting time of positive change and growth, this period of transition also is marked by a spike in the engagement in a variety of risk-taking behaviors, with the potential for very serious life-altering negative consequences. In their classic book on the topic, Jessor and Jessor (1977) defined risk taking as engagement in ‘‘behavior that is socially defined as a problem, a source of concern, or as undesirable by the norms of conventional society and the institutions of adult authority, and its occurrence usually elicits some kind of social control response’’ (p. 33). Focusing more on the consequences of such behavior, definitions of risk taking have also taken into consideration the possibility of positive outcomes and thus have focused on the balancing of potential for harm or danger to the individual with potential achievement or reward (Byrnes, Miller, & Schafer, 1999; Leigh, 1999). This latter view is important because it leaves room for the influence of a variety of factors that affect an emerging adult’s willingness to take risks, including the potential gain from risks both in terms of positive and negative reinforcement and the corresponding opportunity costs for an unwillingness to take risks. Also relevant are developmental norms (e.g., college student drinking) that may impact the occurrence of risk behavior at one life stage and the termination of that behavior in the next life stage (Smith, Molina, & Pelham, 2002).
The goal of this chapter is to consider key risk-taking behaviors specific toemerging young adults with a focus on substance use, risky sexual activity, and delinquent behaviors (e.g., gambling, reckless driving).We begin with a sample case vignette and a review of prevalence/epidemiology, follow with etiology across various domains of biology and behavior, leading into state of the art assessment strategies for understanding, predicting, and preventing risk behavior. Finally, we conclude with a synthesis of the previous sections and relevant practice guidelines.
Ty is an 18-year-old, third-generation Japanese American first diagnosed in elementary school with attention-deficit/hyperactivity disorder (ADHD) which was treated with stimulant medications. Throughout his childhood and high school years, Ty lived a regulated life at home. He was closely monitored by his parents and was required to adhere to strict rules (e.g., required to come home right after school and abide by an early curfew, and only allowed to spend the night at friends’ houses on occasion). His parents provided structure with his school work, setting up time for him to do his work each day and providing close supervision and instruction. He made it through high school with average grades and only minor experimentation with alcohol and some delinquency-related behavior (e.g., physical fight with a peer, spray painting). He left for college with plans to continue a relationship with his girlfriend of 6 months who was starting her junior year in high school. Ty functioned well upon entry into college, but his impulsive and inattentive symptoms associated with ADHD made it difficult for him to make safe choices within the context of the college environment. He was faced with the challenge of succeeding academically and socially in a relatively unstructured environment while living away from home for the first time. He no longer had his parents supervising his behavior and he was presented with an array of social activities that involved opportunities for risk taking. Ty’s struggle with impulse control and planning produced an unsustainable lifestyle and made successful navigation of this new environment particularly challenging. Ty began to engage in binge drinking, attending parties at which he drank heavily (i.e., over five drinks in one evening) three to four times a week. As his drinking became more frequent, he became less able to keep up with his school work and he began a dangerous pattern of procrastination that culminated in nightly Internet gambling. Further, these activities only exacerbated his fears of commitment and he began to lose touch with his girlfriend from home and instead sought more casual sexual encounters, sometimes without using any form of protection.
Throughout his first semester, his grades slipped and he ended up on academic probation. Ty felt embarrassed by his school performance and began to experience a persistent negative mood. During the next semester, more varied and frequent risk behaviors began, serving the function of helping him cope with his guilt, shame, and other negative feelings. These behaviors included marijuana use that started socially with friends but soon became more isolative as he began to use more often than his friends. As he started to miss classes, he needed to cram more heavily for exams to make up for the missed class time. This pattern of missed classes and need for cramming led to Ty increasing his dose of stimulants without supervision by his physician.
Sensing a change in their son, including a more sullen demeanor and frequent shortness during phone calls, Ty’s parents became increasingly concerned. Because they were paying his college tuition, his parents continued to have some control over their son and they refused to continue paying unless he moved home for the spring semester to refocus himself and begin seeing a therapist at the University Counseling Center. Upon starting the spring semester, Ty was set up with services for his ADHD, including continued involvement with a therapist that helped him with organization techniques and a plan for studying. As Ty’s negative mood decreased, he was less inclined to engage in risk-taking behavior as a coping technique. His involvement with online gambling and marijuana ceased, largely due to closer supervision from his parents at first, and diminished need over time as he began to reengage with people and activities he enjoyed prior to starting college, including reconnecting with his high school girlfriend. Soon he ‘‘grew out of’’ his interest in illicit drugs, and although he still drank on occasion, it was at a socially appropriate level.
(p. 41) Prevalence Rates/Epidemiology
Smith and colleagues have suggested that, in contrast to alcohol use during adolescence, alcohol use may be ‘‘normative’’ during young adulthood. Research consistently shows that people tend to drink the heaviest in their late teens and early to mid-twenties (Fillmore, Hartka, & Johnstone, 1991; Naimi, Brewer, & Mokdad, 2003). Specifically, young adults are especially likely to engage in binge and heavy drinking (SAMHSA, 2004). According to National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) data, about 46% of young adults engaged in drinking that exceeded the recommended daily limits at least once in the past year, and 14.5% (3.9 million) had an average consumption that exceeded the recommended weekly limits (Dawson, Grant, Stinson, & Chou, 2004). This peak in heavy drinking in young adulthood is also evident in international samples (e.g., Casswell, Pledger, & Hooper, 2003). Such heavy drinking in young adulthood is associated with high-risk activities (p. 42) and negative consequences, such as risky sexual behavior, diminished academic performance, physical aggression, and sexual victimization (Wood, Read, Palfai, & Stevenson, 2001). Additionally, young adult drivers, ages 21–34, have the highest rates of alcohol involvement in fatal crashes (NHTSA, 2006).
Prominent attention has been given to the consequences of excessive drinking by college students (College Task Force, 2002; Weschsler et al., 2002), as 31% of undergraduate college students meet criteria for alcohol abuse according to the Diagnostic Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American Psychiatric Association, 1994), and another 6% can be classified as DSM-IV alcohol-dependent (Knight et al., 2002). In 2001, 600,000 college students were unintentionally injured from alcohol-related injuries, and 696,000 were assaulted or hit by another drinking college student (Hingson, Heeren, Winter, & Wechsler, 2005). These statistics have contributed to the view that the college environment may function as a facilitator/incubator of excessive drinking, alcohol abuse, and alcohol dependence. However, it is important to clarify that although some research indicates higher rates of drinking and alcohol-related problems in college attendees (e.g., Slutske et al., 2004), other studies indicate greater likelihood of alcohol abuse and dependence among nonattendees who face many similar changes, including weakening parental control as well as a host of other challenges related to an earlier entry into adult roles (Harford, Yi, & Hilton, 2006).
Illicit Drug Use
Similar to alcohol use, during the period of emerging adulthood, illicit drug use also increases, peaks, and then declines for most young people (Arnett, 2005; Bachman et al., 2002; Bachman, Wadsworth, O’Malley, Johnston, & Schulenberg, 1997; Chen & Kandel, 1995). According to the 2002 National Survey on Drug Use and Health (NSDUH) (SAMHSA, 2003), rates of past month illicit drug use climbed steadily for youth from ages 12 to 17, peaked among 18-to 20-year-olds, and remained high for those between ages 21 and 25 before dropping for persons ages 26 through 29. In addition to continuing use, initiation of substances also occurs during this time period; for example, one-third of new marijuana users start using after age 17 as do about 70% of cocaine users (Volkow, 2004). In terms of variation in use among college attendees and non-attendees, illicit drug use has been increasing on college campuses since the mid 1990s (Mohler-Kuo, Lee, & Weschler, 2003). However, college students differ only modestly from their non-college peers in their rate of drug use and types of drugs used (Johnston, O’Malley, Bachman, & Schulenberg, 2005). The annual prevalence (i.e., use of the drug in the past year) for the use of any illicit drug among college students is 36%, compared to 39% of young adults not attending college (Johnston et al., 2005), and when considering only drugs other than marijuana rates are 19% for college students and 24% for terminal high school graduates. Thus, illicit drug use among college students does not appear to exceed rates of use in the general young adult population, and for certain drugs, rates appear to be somewhat lower in the college student population.
Cigarette smoking is amajor public health concern in the United States, as well as internationally. In the UnitedStates, it is the leading cause of preventable death in the nation, accounting for over 440,000 premature deaths yearly (Centers for Disease Control and Prevention, 2002b). Rates of smoking in the 1990s declined in all age groups except ages 18–24 (Hebert, 2004), and one-third of this age group is comprised of college students (U.S. Bureau of the Census, 1997). Cigarette smoking occurs less frequently in the college student population than in the general young adult population (5.6% vs. 16%; Johnston et al., 2005). Similar rates of cigarette smoking have been found in young adult populations internationally (e.g., China; Kumra & Markoff, 2000), and young adult cigarette smoking represents a significant longterm public health issue in many countries (Peto & Lopez, 2000).
Risky Sexual Behavior
Earlier sexual maturity, later marriage, and emphasis on education have contributed to a (p. 43) much longer period of time between the onset of sexual maturity and marriage. The longer this period extends, the more likely it is that unmarried adolescents and young adults will become sexually active (Michael, Gagnon, Laumann, & Kolata, 1994). In terms of risky sexual behavior, those under the age of 25 are more likely than other age groups to have unprotected intercourse, to have multiple sex partners, and, particularly for young women, to choose older sex partners (Centers for Disease Control and Prevention, 2002a; Valois, Oeltmann, & Waller, 1999).
To specifically address the college population, sexual behavior among college students has been assessed in two studies utilizing national samples of college students. The first study, the National College Health Risk Behavior Survey (NCHRBS), was conducted in 1995, and the second study, the National College Health Assessment (NCHA), was conducted in 2003. While both studies surveyed the same population, they asked questions aimed at slightly different types of information and together they provide a comprehensive picture of risky sexual behavior in the college student population. According to the NCHRBS (Douglas et al., 1997), 29.6% of college students who engaged in sexual intercourse during the 3 months prior to the survey reported using a condom during their last sexual intercourse, and 79.8% reported using some form of contraception during their last sexual intercourse. Of this group, 27.9% reported using a condom most of the time or always. The NCHA (The American College Health Association, 2005) contained questions specific to negative outcomes associated with risky sex practices. In response to this survey, 26.2% of college students reported ever being tested for human immunodeficiency virus (HIV) infection, 10.1% of sexually active women reported using emergency contraception within the past school year, 2.6% of female students who had vaginal intercourse in the past year reported becoming pregnant unintentionally, and 2.0% of male students who had vaginal intercourse in the past year reported impregnating someone unintentionally. The data collected through these national surveys show that risky sex practices are evident on college campuses, and these findings extend to similar settings internationally (e.g., Bosompra, 2000; Jenkins et al., 2002). As with alcohol, it is important to note that risky sexual behavior is not limited to those attending college. In addition to unwanted pregnancy, risky sexual behavior is of great concern given its clear connection to STDs (Centers for Disease Control and Prevention, 2002a) as well as death from AIDS following infection with HIV where young adults account for the majority of cases (60% of male; 64% of female AIDS cases; Centers for Disease Control and Prevention, 2000).
Other Risk-Taking Behaviors
The emergence of young adulthood also marks the period at which many delinquent and potentially criminal behaviors peak before dropping near the end of young adulthood (Arnett, 1995). In fact, the number of violent acts committed by high school seniors has increased almost 50% over the past two decades, as have violent crime arrests, particularly for aggravated assault and robbery (U.S. Department of Health and Human Services, 2001). While not all delinquent adolescents continue such risk behaviors in young adulthood, a longitudinal study examining desistence from serious delinquency in young adulthood found that approximately 60% of delinquent adolescents continued in behaviors from reckless driving through the violent use of a weapon. Further, serious delinquency in late adolescence was identified as a risk factor for continued delinquency into young adulthood (Stouthamer-Loeber, Wei, Loeber, & Masten, 2004).
Gambling is another risk behavior that is prevalent in adolescents (Derevensky & Gupta, 2007; Jacobs, 2000) as well as young adults (Blinn-Pike, Lokken Worthy, & Jonkman, 2007; LaBrie, Shaffer, LaPlante, & Wechsler, 2003). Research indicates that approximately 15.3 million adolescents in the United States and Canada gamble and 2.2 million exhibit serious consequences from gambling (Jacobs, 2000). Similar rates of gambling in young adulthood have been found in other countries (e.g., the United Kingdom; Wardle et al., 2007). Further, problem gambling during adolescence has been shown to be a significant predictor of problem gambling during young adulthood (Winters, Stinchfield, (p. 44) Botzet, & Anderson, 2002). Prevalence rates of gambling behavior are particularly high at the onset of adulthood, as the characteristics of the college setting may exacerbate gambling behavior in young adults (Winters, 2002). A meta-analysis of college student gambling studies indicates a high prevalence of lifetime problem gambling in this population (5.6%), which is almost triple the prevalence rate in older adults (Shaffer & Hall, 2001). Problem gambling on college campuses has been associated with a wide range of negative consequences and risk behaviors, including a greater likelihood to engage in risky sexual behavior and substance use, as well as experiencing more negative consequences of use (Engwall, Hunter, & Steinberg, 2004; LaBrie et al., 2003).
Gender and Race/Ethnicity Considerations
Factors such as gender, race, and socioeconomic status recently have been shown to vary in several important ways with risk behavior. The greatest amount of work has focused on gender with an interesting interaction seemingly evident (Byrnes, Miller, & Schafer, 1999). Risk behaviors are fairly equal across boys and girls in early adolescence, with girls evidencing slightly higher rates. However, through high school a reversal occurs such that risk behavior is greater in males as they enter adulthood. The Monitoring the Future Study has been tracking the substance use habits of high school students, both during high school and after graduation, since 1975 (Johnston et al., 2005). Their results show that in 12th grade, there are generally higher proportions of males than females involved in illicit drug use, especially heavy drug use (Johnston, O’Malley, Bachman, & Schulenberg, 2007). As discussed above, this study suggests that there is little gender difference in use in the lower grades. In fact, for some drugs females have slightly higher rates of annual use in 8th grade (Johnston et al., 2007).
In terms of race and ethnicity, the Monitoring the Future Study indicates that at all three grade levels (8th, 10th, and 12th) African American youth have considerably lower rates of substance use (alcohol, illicit drugs, cigarettes) than do whites (Johnston et al., 2007). By 12th grade, white students have the highest lifetime and annual prevalence-of-use rates among the three major racial/ethnic groups (white, African American, and Hispanic) for many substances (Johnston et al., 2007). In addition, while drinking among whites tends to peak around ages 19–22, heavy drinking among African Americans and Hispanics peaks later and persists longer into adulthood (Caetano & Kaskutas, 1995).
In addressing gender and race/ethnicity differences, an interesting area of disparity is among one outcome of risk behavior: sexually transmitted diseases (STDs). Research suggests that females, Hispanics, and non-Hispanic blacks are disproportionately affected by STDs (Tucker Halpern, Young, Waller, Martin, & Kupper, 2004). For example, in 2001, the gonorrhea infection rate among 15- to 19-year-old black females was almost twice as high as that for black males, and 18 times as high as the rate for white females (Centers for Disease Control and Prevention, 2002a). Black and Hispanic women account for 79% of all reported HIV infections among 13- to 19-year-old women and 75% of HIV infections among 20- to 24-year-old women in the United States, although together they represent only about 26% of U.S. women of these ages (Centers for Disease Control and Prevention, 2002a). Clearly future research is necessary to understand these dangerous disparities.
Less research is available on socioeconomic status. In the Monitoring the Future Study, socioeconomic status is indexed by parental education level. The study shows that by senior year there is little correlation of family socioeconomic status with the use of most substances. The authors suggest that this speaks to the extent of substance use across all social strata (Johnston et al., 2007); however, the potential risk factors for those in higher SES groups may differ from the documented risk factors for those in lower SES groups. It will be important for future research to examine the interactions among these variables (gender, race/ethnicity, and SES).
Research suggests that risk behaviors, with specific relevance to substance use, have a (p. 45) significant genetic component (Duaux, Krebs, & Poirier, 2000). It is estimated that 40%–60% of the vulnerability to addiction can be attributed to genetic factors (Goldman, Oroszi, & Ducci, 2005; Hiroi & Agatsuma, 2005; Kreek, Nielsen, Butelman, & LaForge, 2005). Predisposition to addiction may be due to both genetic variants that are common across addictions and to those specific to a particular addiction (Kreek et al., 2005). Genetic factors may be involved in direct drug-induced effects, including alteration of a drug’s effect at a receptor or a drug’s absorption, distribution, metabolism, and excretion (Kreek et al., 2005).
Alcohol consumption is considered to be partially determined by genetic factors (Han, McGue, & Iacono, 1999; Heath, 1995; Heath et al., 1997; McGue, 1993, 1994), with a particularly strong genetic determination among men (Grant et al., 1999). Walters (2002) reported results from a meta-analysis of 50 family, twin, and adoption studies, in which genetic factors accounted for 20%–26% of the variability in alcohol misuse. However, Johnson, Vernon, Harris, and Jang (2004) suggest that genetic contributions to the etiology of alcoholism are not best accounted for by a simple univariate herit-ability estimate. For example, genetic factors may account for as much as 60% of the association between antisocial personality and alcohol dependence (Grove et al., 1990; Jang, Vernon, & Livesley, 2000; Slutske et al., 1998), and the genetic correlation between alcohol consumption and stimulus-seeking behaviors ranges from .33 and .45 (Jang et al., 2000; Mustanski, Viken, Kaprio, & Rose, 2003). Alcohol consumption has also been proposed to have a significant genotype by environment interaction. The herit-ability of alcohol consumption increases with age (Viken Kaprio, Koskenvuo, & Rose 1999), suggesting the reasons for alcohol consumption early in life may be more related to environmental determinants such as availability or opportunity.
A genetic basis for drug abuse has also been investigated thoroughly. For example, cannabis use is suggested to be the result of both genetic and environmental factors (Cadoret, 1992; Kendler, Karkowski, Neale, & Prescott, 2000; Kendler, Prescott, Myers, & Neale, 2003; Miles, Van den Bree, & Gupman, 2001; Tsuang et al., 1996, 1999; Van den Bree, Svikis, & Pickens, 1998). Miles et al. (2001) suggested a moderate heritability of cannabis use (.31) and a more substantial effect of the shared environment (.47). These findings support earlier findings that suggested an approximately equal distribution of variance estimates between genetic determinants, the shared environment, and unique environmental events (Tsuang et al., 1996). Tsuang et al. (1999) proposed a stage model of drug abuse that suggests a hierarchical progression toward drug abuse: exposure, initiation of use, continuation of use, regularity of use, substance abuse, and then addiction. The transition from regular use to heavy use and abuse has been demonstrated to be strongly genetic (Cadoret, 1992), with heritability ranging from 60%–80% (Kendeler, Karkowski, Neale, & Prescott, 2000).
Fisher (1958) presented the earliest evidence for a genetic determination of tobacco use, demonstrating a higher concordance rate among MZ twins than DZ twins. More current research suggests that smoking initiation and persistence demonstrates substantially greater genetic determination in men than women (Han, McGue, & Iacono, 1999; Madden et al., 1999). Li, Cheng, Ma, & Swan (2003) report that smoking initiation demonstrated a significantly higher genetic basis among women (55%) than among men (73%), while smoking persistence demonstrated a non-significantly higher genetic basis among men (57%) than among women (46%). Tobacco has also been linked to dopamine receptor polymorphisms. Research focuses on the importance of the DRD2 gene in the prediction if individuals most at risk for smoking initiation (Bierut et al., 2000; Comings et al., 1996; Wu, Hudmon, Detry, Chamberlain, & Spitz, 2000; Yoshida et al., 2001).
Numerous studies have examined the influence of genetics on the development of pathological gambling. Twin and family studies have suggested it to be hereditary, with genetic factors accounting for 35% to 54% of the risk of developing pathological gambling (Eisen et al., 1998). Genetic contribution has been shown to be stable over a 10-year period (Xian et al., 2007) and higher inmen (Shah, Eisen, Xian, & Potenza, 2005; Walters, 2001; Winters & Rich, 1998). Research at the molecular level has demonstrated associations between (p. 46) pathological gambling and DRD1 (Sabbatini da Silva Lobo et al., 2007), as well as allele variants of the serotonin transporter gene and the monoa-mine-oxidase A gene, particularly in severe males (Iban˜ez, Blanco, Perez de Castro, Fernandez-Piqueras, & Saiz-Ruiz, 2003; Iban˜ez, Perez de Castro, Fernandez-Piqueras, Blanco, & Saiz-Ruiz, 2000; Perez de Castro, Iban˜ez, Saiz-Ruiz, & Fernandez-Piqueras, 1999).Research has also pinpointed a shared genetic vulnerability in pathological gamblers and alcohol users, with alcohol dependence accounting for 12% to 20% of the genetic variation in the risk for pathological gambling (Slutske, 2000).
It is important to stress that evidence for genetic vulnerability alone is unlikely sufficient for development of risk behaviors, and they often combine with other factors to confer risk, including gene–environment interactions and gene–gene interactions. The interaction of genes with other factors, especially environment, will be addressed in more detail below.
A number of behavioral and cognitive changes that take place in adolescence through young adulthood may be related to maturation in the brain (Spear, 2000). Research shows that the brain continues to develop throughout adolescence and well into young adulthood. There has been a specific focus on the dopamine systems and associated mesolimbic and mesocortical brain areas. Dopamine, a neurotransmitter associated with the reward system in humans, has significant implications with regard to engagement in risk-taking behaviors. Individuals vary in their number of dopamine receptors and this variation can lead to differing levels of risky behavior (Kalat, 2004). Drugs and novel situations activate reward systems in the brain, and the mesolimbic dopamine reward pathway is a critical link that mediates drug reward (Koob, 2006). Novelty seeking and risky behaviors cause a release of dopamine in the nucleus accumbens across individuals, but there are fairly substantial differences in their ‘‘need’’ for novelty. Genetic differences in novelty-seeking and drug-seeking behavior may be mediated by differences in the mesolimbic dopamine systems (Bardo, Donohew & Harrington, 1996). There is some debate as to whether increases in drug-seeking behavior are related to elevations or reductions in mesolimbic DA activity. The traditional DA hypothesis of reward suggests that DA systems should be positively related to drug-seeking behavior (Spangel & Weiss, 1999). Yet others suggest that enhanced vulnerability to drug use and abuse is associated with a reward deficiency syndrome. Due to functional deficits in mesolimbic dopamine systems, individuals find reinforcing stimuli less pleasurable than others, leading them to seek out drugs and novelty as a behavioral remediation of reward deficiency. This deficiency may be due to lower than normal levels of extracellular dopamine. Another possible cause for this deficiency may be lower D2 receptor levels caused by the expression of the A1 allele on the D2 receptor gene (Spear, 2000). This allele increases the probability that a person will become alcohol dependent or will engage in a variety of pleasure-seeking behaviors (Kalat, 2004). Those with an alternate form of the D4 receptor have been shown to have novelty-seeking personalities (Donohew, Bardo, & Zimmerman, 2004). This may lead to higher levels of impulsivity, exploratory behaviors, and quick-temperedness. Studies have implicated D4 receptors in heroin addiction and found altered distributions allelic variants of the gene encoding the D4 receptor in alcoholics and pathological gamblers (Zuckerman & Kuhlman, 2000), although not consistently across studies.
Another factor that is linked to novelty seeking and drug use is level of monoamine oxidase (MAO). MAO is an enzyme that is involved in the catabolic degradation of the monamine neurotransmitters dopamine, sero-tonin, and norepinephrine. There are two forms of MAO: MAO-A and MAO-B. MAO-B is closely tied to the regulations of dopamine, while MAO-A is more involved in the regulation of serotonin and norepinephrine. MAO-B is negatively correlated with sensation seeking (low levels of MAO-B is correlated with high levels of sensation seeking). Platelet levels of MAO-B change slowly as a function of age. Levels are the lowest in adolescence and increase with age. Levels are consistently higher in women than men at all ages, corresponding with the finding that men tend to exhibit higher levels of sensation seeking. Low platelet (p. 47) levels of MAO-B are linked with higher levels of tobacco, drug, and alcohol use as well as higher incidence of criminal offenses (Zuckerman & Kuhlman, 2000). Low levels of MAO-B would lead to slower degradation of dopamine, ultimately increasing dopamine levels in the brain. Studies have shown that rats with high levels of novelty seeking had higher levels of dopamine activity in the nucleus accumbens both under basal conditions and during novel stimulation (Dellu, Piazza, Mayo, LeMoal, & Simon, 1996). If these findings hold true for humans, people with low platelet levels of MAO-B may be more likely to engage in novelty-seeking and drug-seeking behaviors.
The dopamine system in the brain is one of many neural systems that undergo developmental alterations in adolescence. Like many of the other systems, the dopamine system is sensitively activated by stressors. Stressors can activate dopamine projections to the prefrontal cortex as well as to themesolimbic brain regions. Receptors for glucocorticoids (stress hormones) have been identified in the rodent brain on dopaminergic neurons in several areas of the brain, including the nucleus accumbens. Stress-induced increases in glucocorticoids may activate dopamine transmission, thus facilitating drug-taking behaviors. Stress can lead to higher activity in the meso-limbic dopamine system, which is positively related to drug-seeking behavior (Spear, 2000).
Given the greater freedom that may be experienced by young adults, the impact of personality may be especially salient for this group. Sensation seeking is a personality trait defined by ‘‘the seeking of varied, novel, complex, and intense sensations and experiences, and the willingness to take physical, social, legal, and financial risks for the sake of such experience’’ (Zuckerman, 1979). Numerous studies have found sensation seeking to be related across multiple domains of risk-taking behaviors (Caspi et al., 1997; Lejuez et al., 2002, 2007; Zuckerman et al., 1972, 1994).
In addition to the correlations with sensation seeking, the construct of impulsivitymay underlie risk-taking behaviors (e.g., Lane et al., 2003). Specifically, impulsivity has been linked to substance use vulnerability, frequency, severity including social and emotional consequences, and dependence (e.g., Allen, Moeller, Rhoades, & Cherek, 1998; Fishbein, Lozovsky, & Jaffe, 1989; King, Curtis, & Knoblich, 1991; Moeller et al., 2001; 2002; Monterosso, Ehrman, Napier, O’Brien, & Childress, 2001; Patton, Stanford, & Barratt, 1995;Petry, 2001).One difficulty in examining impulsivity and its relationship with the other key variables is the multidimensional nature of the construct (Evenden, 1999; Whiteside & Lynam, 2001). Definitions of the construct include, but are not limited to, the inability to delay gratification (Mischel, Shoda, & Rodriquez, 1989), the process of discounting a reward as a function of delay (Ainslie, 1975; Reynolds et al., 2007), and the inability to inhibit prepotent responding (Logan, 1994; Newman, Patterson, & Kosson, 1987). Several tasks and self-report instruments have been developed to measure each of these dimensions. Despite the recognized multidimensionality of impulsivity, most studies examining the construct study one dimension in isolation (for an exception, see Lane et al., 2003). Thus, it is difficult to speculate on the generalizability of the results across other dimensions of impulsivity and, more importantly, on howspecific components ofimpulsivity are related to risk-taking behavior.
Alargemajority ofwork on personality and risk behavior has focused on individual behaviors and specific personality variables, indicating a lack of covariance across risk behaviors and the personality constructs that may underlie these behaviors (Anderson et al., 1993; Tolan & Loeber, 1993). However, more recent work has begun to focus more on the common links across these behaviors, including a higher order aspect of personality referred to as the externalizing factor (Krueger et al., 2002; Krueger, Markon, Patrick, & Iacono, 2005). Further work aimed at understanding the functional similarities across risk behaviors and the personality factors underlying them isof great importance, especially in terms of improving assessment, prevention, and treatment of individual and clusters of risk behaviors.
Externalizing disorders such as childhood ADHD and conduct disorder (CD) have been (p. 48) pinpointed as developmental precursors to problematic engagement in risk-taking behavior. ADHD is characterized by developmentally inappropriate levels of inattention, hyperactivity, and impulsivity, whereas CD is characterized by a persistent pattern of disregard for rules and the rights of others (APA, 1994). Each independently has been identified as an early risk factor for the emergence of risky behavior in adolescence and adulthood, and their combination appears to pose a higher risk than either disorder alone (Flory, Milich, Lynam, Leukefeld, & Clayton, 2003; Flory, Molina, Pelham, Gnagy, & Smith, 2006; Molina, Smith, & Pelham, 1999; Thompson, Molina, Pelham, & Gnagy, 2007). Research indicates that children with ADHD report higher levels of alcohol, tobacco, and illicit drug use in adolescence compared to same-aged adolescents without a history of ADHD, and severity of ADHD symptoms in childhood also predict levels of substance use in adolescence (Molina & Pelham, 2003).
CD also has been identified as a strong predictor of early alcohol initiation and alcohol dependence (Sartor, Lynskey, Heath, Jacob, & True, 2007), as well as other substance and illicit drug use, and those with CD and ADHD demonstrate the highest levels of use (Flory et al., 2003). Research has also indicated that the link between childhood ADHD and alcohol use disorder (AUD) is only evident in older adolescents between 15 and 17 years of age (Molina, Pelham, Gnagy, Thompson, & Marshal, 2007) and continues into adulthood (Biederman et al., 2006). Additionally, childhood ADHD has been demonstrated to be a predictor of multiple risky sexual behaviors, including earlier initiation of sexual intercourse, higher rates of reported sexual partners and casual sex, and higher rates of pregnancy through early adulthood (Flory et al., 2006). Research has also linked childhood ADHD to driving-related risk behaviors, particularly when symptoms persist and CD is co-occurring (Thompson et al., 2007). Given the relationship between ADHD, CD, and a wide range of risk-taking behaviors, it is important to evaluate the presence of these disorders starting in childhood to better monitor and intervene at the onset of adolescent risk taking.
Psychopathology, Stress, and Coping
A wealth of literature suggests that affective distress is related to risk-taking behavior. There is a high comorbidity between substance use disorders and mood and anxiety disorders such as depression (Brooner, King, Kidorf, & Schmidt, 1997; Griffin, Weis, Mirin, & Lange, 1989; Weiss, Kung, & Pearson, 2003). Both cross-sectional (Clark, Lynch, Donovan, & Block, 2001) and longitudinal studies (Cooper, Wood, Orcutt, & Albino, 2003; Krueger, 1999; Wills, Sandy, & Yaeger, 2001) have demonstrated an association between trait negative affect and initiation of substance use, as well as severity of substance-related problems (e.g., Johnson & Pandina, 1993; Labouvie, Pandina, White, & Johnson, 1990). Although some evidence exists for a relationship between negative affect (e.g., depression, anxiety, anger) and sexual risk behavior, several findings have been inconclusive (Crepaz & Marks, 2001). It is hypothesized that those individuals who rely on dysfunctional styles of coping in the face of negative emotions are less able to effectively regulate their negative mood states, thus becoming vulnerable to the immediate relief promised by various risky behavioral alternatives (Westen, 1994). In turn, engagement in risky behaviors often brings relief (e.g., via distraction or euphorigenic effects of substances), thus enhancing the attractiveness of such behavior for future situations. Baker, Piper, & McCarthy (2004) provide a compelling conceptualization of these processes within the context of a negative reinforcement approach. In support of this type of conceptualization, Cooper, Agocha, and Sheldon (2000) found that drinking to cope with negative feelings was a good predictor of heavy drinking as well as drinking problems in 19- to 25-year-olds. Thus, although it is important to note that young adults are likely to engage in risk behavior that is ‘‘positive’’ or celebratory (e.g., Read, Wood, Kahler, Maddock, & Palfai 2003), the pressures and stresses of young adulthood increase the likelihood of risk behavior aimed at coping with stress and other forms of negative affect.
At a biological level, one’s response to stress also appears to be relevant. The hypothalamic-pituitary-adrenal system (HPA axis), the anatomical structures that regulate levels of cortisol in (p. 49) the body in response to stress, should increase production of cortisol to mediate alarm reactions to stress, thus facilitating an adaptive phase in which alarm reactions are suppressed and the body can attempt counter-measures. Healthy regulation of the HPA axis is necessary to cope with life stressors, and low levels of cortisol in young adults with externalizing behavior may suggest HPA axis dysfunction. Further, human and animal studies have also found support for the role of HPA axis dysfunction in substance use, indicating that altered HPA axis response can lead to increased drug craving and acquisition (Brady & Sinha, 2005; Sinha, Garcia, Paliwal, Kreek, & Rounsaville, 2006). Research indicates that cortisol response may also be hereditary; non-substance-using subjects with a family history of alcoholism had lower cortisol responses than those without a family history of alcoholism, particularly when subjects had antisocial characteristics (Sorocco, Lovallo, Vincent, & Collins, 2006). Additionally, another study examining age of cannabis use initiation identified lower HPA axis activity in the early-onset group compared to subjects who had initiated cannabis use at a later age (Huizink, Ferdinand, Ormel, & Verhulst, 2006). Within the context of the various stressors associated with the transition to young adulthood outlined above, poor HPA axis functioning is likely a key factor in the development and continued use of risk behaviors, including substances to modulate the effects of stress.
Data from both animal (Pryce, Bettschen, & Feldon, 2001) and human work (for review, see Fergusson, Lynskey, & Horwood, 1996) indicate that early childhood experience can have enduring consequences on the emergence and continuance of risk behavior. One specific type of adverse early experience is abuse, which has consistently been found to predict the increased likelihood of risk behaviors, including substance use and risky sexual behavior. Research linking child abuse with later risk behaviors has focused largely on childhood sexual abuse, with studies indicating that sexually abused individuals are at an increased risk of later engagement in risky sexual behavior (such as multiple short-term sexual encounters; exchange of sex for money, drugs, or shelter; and unprotected sex), as well as substance use (e.g., Paolucci, Genuis, & Violato, 2001). However, sexual abuse rarely occurs in the absence of a broader social context of multiple adversities, including other forms of abuse such as emotional and physical (e.g., Bornovalova, Gwadz, Kahler, Aklin, & Lejuez, 2008; Dubo, Zanarini, Lewis, & Williams, 1997). Moreover, preliminary evidence indicates that physical and emotional abuse uniquely contribute to risk behaviors (e.g., Medrano, Hatch, Zule, & Desmond, 2003). The mechanism through which childhood abuse later influences risk-taking behavior has begun to be examined, including psychosocial variables such as deficits in emotion regulation and coping (Miller & Lisak, 1999), insecure attachment (Schindler et al., 2005), disinhibition variables of risk-taking propensity and sensation seeking (Bornovalova et al., 2008), and neurocognitive changes (Weiss & Wagner, 1998), with recent movement toward integrative theories considering the interactive roles across domains.
Pressure from one’s peers and the desire to fit into a group greatly influence behavior (Festinger, 1950; Petraitis, Flay, & Miller, 1995). The influence of peer relationships substantially increases during adolescence. Research has suggested that many youth believe that desirable social outcomes, such as peer acceptance or peer support, will occur as a result of using substance, and they are, therefore, motivated to initiate substance use (Jenkins, 2001; Sussman et al., 1995). There is evidence that the number of friends who use illicit drugs (Jenkins & Zunguze, 1998) and smoke cigarettes (Wang et al., 1997) is positively associated with one’s own illicit drug use and smoking, respectively. For example, smoking status of peers has shown differing levels of importance as a predictor of smoking behavior in adolescents across various studies. Many studies have found that a higher proportion of smoking friends predicted smoking initiation (e.g., Bauman, Carver, & Gleiter, 2001; Urberg, Degirmencioglu, & Pilgrim, 1997). Once the individual begins more frequent participation in a particular risk behavior, the individual may begin to select (p. 50) peers that share similar values and problem behavior, while also spending less time with less risky peers who may start to share fewer interests and who may even be disapproving of such risk behavior (Valente, Gallaher, & Mouttappa, 2004).
In addition to actual peer influence, certain behaviorsmay be supportedbyperceived behavior of others and the resulting social norms that accompany these perceptions. For example, the belief that ‘‘everyone’’ is drinking and drinking is acceptable is one of the strongest correlates of drinking among young adults and the subject of considerable research (e.g. Jackson, Sher, & Park, 2005). This and other types of norm mispercep-tions appear to contribute to problem drinking among young adults. For example, within the college setting, many students believe campus attitudes are much more permissive toward drinking than they are in reality, believe other students consume alcohol more frequently an in higher quantities than they actually do (Borsari & Carey, 2001, 2003; Perkins, 2002), and tend to misjudge the attitudes of others toward alcohol use and drunkenness (Read, Wood, Davidoff, McLacken, &Campbell, 2002). In a study conducted by Wood and colleagues (2001), social influence was cited as the strongest correlate of alcohol use and abuse. Social influence was broken down into two categories: (1) active: explicit invitations and (2) passive: perception and interpretation of drinking within a group (social modeling and mispercep-tion of peer norms).
Influence from the Virtual Environment.
An inter-estingemerging environmental risk factor for risk-taking behavior is the Internet. The Internet can provideolder adolescentswithaccess tomore risky peer groups, thereby increasing access to problem behaviors such as illicit substance use and risky sexual behavior. Gambling may be an especially great risk on the Internet because the risk behavior can occur at that moment in the virtual environment and does not require the transition to the real world as would be the case for most other risk behaviors. Research suggests that Internet gambling poses an increased risk for all ages to develop problem gambling, due to the effects of increased accessibility, use of electronic cash, as well as its solitary nature (Griffiths & Park, 2002; Wood, Griffiths, & Parke, 2007). Yet adolescent and young adult students may be most vulnerable to the effects of Internet-based problem gambling, given the lack of appropriate regulation of many Internet sites, as well as the frequency of Internet use among students (Derevensky & Gupta, 2007; Wood et al., 2007). Research examining online poker playing in a student sample (n=422) found that 18% of a student sample were problem gamblers, and one-third of the students reported participation in online gambling at least twice per week (Wood et al., 2007).
Environmental Factors Specific to Emerging Adulthood.
The transition to adulthood is a large contextual shift when individuals often move out of the family residence and become increasingly self-supporting (Arnett, 2005). Becoming part of the world of work and/or higher education increases the separation from the family of origin as individuals become part of new contexts. Within emerging adulthood there is cognitive development (e.g., continued brain development, the emergence of personal beliefs and values), changes in one’s identity or self-definition (e.g., exploration of career roles), and affiliative transitions (e.g., shifts in relationships with parents and peers).
As an example of one common new context, a high proportion of young people, about 60%, enter college after graduating from high school (Mogelonsky, 1996). This is a higher proportion than ever before in American history. College students are faced with the challenge of succeeding academically and socially in a relatively unstructured environment, often while living away from home for the first time. They typically no longer have close parental supervision of their behavior and are presented with an array of social activities that involve opportunities for risk taking. The delayed assumption of adulthood roles, the absence of social control agents (such as parents and the high school structure), relatively easy access to alcohol (Wechsler, Lee, Kuo, & Lee, 2000), and immersion in an environment of same-age peers makes the college years a time of heightened engagement in risky behaviors (Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996). Many suggest that the college campus environment itself encourages heavy drinking (Toomey & Wagenaar, 2002).
(p. 51) Aside from factors that enhance risk behavior, it is also important to consider factors that may reduce risk behavior. One such factor is marriage or serious committed romantic relationships. Specific to alcohol use, young married women have the greatest decreases in drinking behavior, and married men, compared with men in all other categories of living arrangements (i.e., living with parents, in a dormitory, alone, or in other arrangements) have the least robust increases (Bachman et al., 1997). Interestingly, divorce, which may occur in young adulthood, has the opposite effect, showing a relationship with increases in alcohol use (Bachman et al., 1997). Being a parent also is related to lower alcohol use for both men and women; however, during pregnancy, most women eliminate their alcohol use, although most of their husbands do not (Bachman et al., 1997). Young adults with serious alcohol problems (those who fit the diagnostic criteria for alcohol dependence) may not be as likely to choose stable roles such as marriage and parenthood, or these milestones may not affect their drinking behavior to the same extent that they affect people with less problematic drinking practices (Matzger, Delucchi, Weisner, & Ammon, 2004). Thus, although the development of a family may differ somewhat across gender, there is clear evidence of its general relationship with decreased risk behavior. With this said, it is important to acknowledge that most studies show correlation and not causation because other third variables may be influencing both greater commitment to family and reduced engagement in risk behavior.
Movement toward Integrative Models across Etiological Dimensions
In expanding the scope of risk factors, it is crucial to develop an understanding beyond the independent influence of these etiological factors, and to develop larger models that consider the interactive effects across these factors. Thus far, the greatest progress has come in the consideration of gene–environment interactions (e.g., Kaufman et al., 2007). There are a number of proposed ways in which genes and environment can interact to influence behavior including cases where (1) genotype increases expression of environmental risk factor, (2) genotype exacerbates effect of environmental risk factor, (3) environmental risk factor exacerbates effect of genotype, (4) both genotype and environmental risk factor are required, and (5) genotype and environmental risk factor each affects risk, creating a combined effect greater than additive (Van Duijn et al., 2005). For example, individuals who experience early life stressors are at risk for depression and anxiety. Several groups have observed an interaction between stressful life experiences and the SLC6A4 genotype in the risk for depression (Caspi, Sugden, & Moffitt, 2003). In particular, individuals homozygous for the s allele of the 5HTT gene often experience depression when environmental adversity including stress is experienced. However, these individuals do not appear to be at a substantially elevated risk in the absence of stress, and individuals with other SLC6A4 genotypes appear more resilient to stress. Stress has also been linked to substance use disorders and other risk behaviors, and neurobiological data suggest an overlap in systems underlying stress response and addiction vulnerability (Kreek et al., 2005). Few studies have directly examined gene–environment interactions in addiction (for an exception, see Kaufman et al., 2007), but several connections point to their potential relevance, including (1) evidence for a direct relationship between the short allele of 5HTT and addiction, (2) the connection of addiction with psychopathology suggesting the value of investigating similar pathways, and (3) the link of addiction to environmental variables, including negative life events, stress, and childhood abuse/adversity.
Moving forward, it is crucial to further develop these models to consider the manner in which gene–environment interactions may be considered together with the other etiological factors outlined above. For example, one possible model includes biological factors such as a genetic vulnerability, poor stress response due to HPA axis dysregulation, and overactivity in the meso-limbic dopamine reward pathway in the presence of stimuli associated with a risky alternative, along with with high impulsive and sensation-seeking traits, excessive stress in the adjustment to adulthood, and an environment that supports risk taking, combining to predict excessive risk behavior peaking in young adulthood. Currently, (p. 52) it seems reasonable to assume that the greater the number of these risk factors, the more likely the individual is to engage in problematic risk behavior, but it is necessary to develop more sophisticated interactive models and strategies to understand specifically how these factors combine to confer risk across individuals and to identify protective factors.
Assessment and Prevention
There are several measures to assess risk taking specific to particular substances. For alcohol and substance use, the Structured Clinical Interview for DSM-IV (SCID; First, Spitzer, Gibbon, & Williams, 1995) is used to diagnose alcohol/ drug abuse and dependence. Yet it is also important to take into account developmental considerations and continuous rather than dichotomous measures. The Core Alcohol and Drug Survey (Core Institute, 1994) has strong psychometric properties and is particularly relevant to the young adulthood developmental stage as well as the college context (Core Institute, 2005). Additionally, it contains items on risky behavior associated with substance use and items specific to the function underlying substance use. The Alcohol Use Disorders Identification Test (AUDIT) and the Drug Use Disorders Identification Test (DUDIT) are useful for obtaining a continuous score that takes into account the quantity and frequency of use and related consequences, which may be useful to understand the level of impairment and severity of use to distinguish from typical college drinking patterns (Saunders, Aasland, Babor, De La Fuenta, & Grant, 1993). Several specific measures are appropriate for assessment of gambling behavior, including the South Oaks Gambling Screen (Lesieur & Blume, 1987) and the Pathological Gambling Modification of the Yale-Brown Obsessive-Compulsive Scale (PG-YBOCS; Pallanti, DeCaria, Grant, Urpe, & Hollander, 2005). Additionally, standard assessments of HIV risk behaviors typically used with adult populations can also be used for HIV risk-taking behaviors in a young adult sample. For example, the AIDS Risk Assessment (ARA; Simpson, 1997) assesses drug use patterns and history as well as sexual risk behavior in 30-day and 6-month time frames.
Considering risk taking behavior more broadly, few comprehensive measures exist, particularly ones that take into account the specific characteristics of a young adult population. The Youth Risk Behavior Surveillance: National College Health Risk Behavior Survey (1997) assesses the occurrence of a variety of health risk behaviors (e.g., tobacco use, unhealthy dietary behaviors, inadequate physical activity, alcohol and other drug use, sexual behaviors that may result in HIV infection, other sexually transmitted diseases, and unintended pregnancies, behaviors that may result in unintentional injuries, such as motor vehicle crashes, and violence, including suicide); however, this assessment does not assess associated impairment or distress. Only measuring occurrence can lead to questions of impairment, and specifically, at what level of occurrence is the behavior dysfunctional. This is more apparent for some behaviors than others (e.g., condom nonuse is arguably always risky, but there is a greater debate about what number of drinks per week/night/sitting constitutes risky drinking behavior). While DSM-IV diagnoses require impairment or distress, diagnoses are not available for many risk behaviors beside alcohol/drug abuse and dependence. Relevant behavior engagement can also be gleaned from diagnoses of CD, yet this diagnosis is not developmentally appropriate. Antisocial personality disorder diagnostic information may also be useful, but it focuses heavily on criminal behaviors.
Although great strides have been made in the prevention of a variety of risk behaviors covered above, the large majority of this work is targeted at children and early adolescents. One area for which prevention has been targeted at young adults and their unique needs is college student drinking. The National Institute of Alcohol Abuse and Alcoholism has published a comprehensive report entitled A Call to Action: Changing the Culture of College Drinking (NIAAA, 2002) that provides research reviews and other crucial information to assist college and university administrators and program managers in their (p. 53) alcohol abuse prevention efforts. In this report, individual alcohol prevention programs are designated as ‘‘Tier 1 strategies,’’ specifying that this method has the most evidence available to support it. Following from the perspective that programs focused on skills development and attention to individual vulnerabilities are more effective than more general information–based approaches (Larimer & Cronce, 2002; NIAAA, 2002; Walters & Neighbors, 2005), these individualized interventions usually combine a motivational interviewing style with personalized feedback. This personalized feedback often includes drinking patterns and percentiles, accurate norms for alcohol use on campus, correction of myths regarding alcohol, negative drinking consequences, and protective behaviors or skills (Martens et al., 2004) that individuals can use to reduce drinking and its consequences (Dimeff, Baer, Kivlahan, & Marlatt, 1999). Thus, although all individuals are targeted, these programs are decidedly individualizing their focus.
Although there is support for individualized prevention programs, one limitation is feasibility. Several practical barriers exist, including the specialized training and ongoing supervision required of prevention staff as well as resources. Another key issue is the methodology required for identification of vulnerabilities for targeting in these individuals prevention programs. Current technologies allow for highly sophisticated identification of biological vulnerabilities, including genetics, HPA axis functioning, and neurobehavioral functioning. At the level of personality, measures of constructs such as impul-sivity and sensation seeking (Zuckerman & Kuhlman, 2000) have shown the importance of personality factors in the relation to risk behavior among adolescents and young adults. Further, as discussed above, more recent efforts have moved to create dimensional higher order factors, such as the externalizing construct to consider the functional similarities across risk behavior and their personality-based determinants. Personality assessment, however, has been limited somewhat by a largely exclusive use of self-report. More recent efforts, however, have moved beyond self-report assessments to include behavioral measures to index impulsivity (Reynolds et al., 2007), risky decision making (Bechara, Damasio, Damasio, & Anderson, 1994), and risk-taking propensity (Lejuez et al., 2007). In addition to being potentially less biased than self-report measures, these tasks provide the opportunity to measure actual behavior (i.e., the participant’s propensity to engage in risk on the task), which is especially useful when considering the influence of other variables that cannot be examined with a self-report measure. For example, the effect of positive or negative affect on risk taking can be examined by comparing behavior on the tasks at a baseline state and then after a mood induction. This approach could also be useful for examining the influence of substance use on subsequent risk behavior using a drug administration paradigm (e.g., will the individuals take more risks in a task under the influence of alcohol). Finally, behavioral tasks are ideally suited for studying cognitive and neurobeha-vioral processes as well as biological stress response changes during their administration. Although these tasks largely have been limited to research use, they are now becoming more commonly used in clinical practice for assessment and intervention planning purposes.
Key Points/Practice Guidelines
There are a number of key points that may be useful in moving forward in our understanding of adolescent risk and its emergence into adulthood. First, risk taking involves the balancing of negative and positive consequences. As such, not all risk behavior is inherently bad and some willingness to take risks in a young adult’s life is crucial for discovering, developing, and consolidating his or her identity (Millstein & Igra, 1995). The line between healthy and unhealthy risk behavior, however, can sometimes blur, especially as even more hazardous behaviors may be commonplace among one’s peers and the consequences of such behavior may seem distant and unlikely. Although most young adults will ‘‘grow out of’’ patterns of risk, some will continue in these patterns with increasing negative consequences. Second, it has become increasingly important when studying risk behavior to take a multifactorial perspective in which multiple influences are considered instead of focusing on a specific (p. 54) indicator or characteristic. Significant comor-bidity between CD, substance use disorders, and mood disorders suggests the potential for multiple synergistic mechanisms (e.g., sensation seeking, impulsivity, poor judgment, negative affectivity) that may increase participation in a wide range of health risk behaviors (Clark & Bukstein, 1998; Zeitlin, 1999). Advancements in understanding the link between biology, environment, and behavior are crucial for moving beyond overly simplistic accounts of risk behavior, with the importance of considering unique circumstances for emerging young adults. Finally, it is useful to apply this mutlifactorial approach in understanding specific vulnerabilities among emerging young adults and developing and utilizing these advancements in clinical assessment toward implementing individualized prevention efforts. Thus, the best strategy for limiting the role of risk behavior is a more integrative biobehavioral approach that considers how all these factors come together to confer differential variability and how we might individually target these differential vulnerabilities. To date, however, efforts to integrate behavior and biology in actual practice are less common.
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