(p. 292) Alcohol Use Disorders in Young Adulthood
Zeke, aged 24 years, works intermittently as a car mechanic. He recently was charged with impaired and dangerous driving and has lost his job. He could not afford his apartment and has returned to live with his father and stepmother on the condition that he get treatment for alcohol problems. Zeke started drinking in high school at age 16. He and his friends soon became daily drinkers, often drinking at lunchtime. He had always struggled with the academic requirements of school despite average intellectual abilities, finding it difficult to maintain his interest and focus. He often forgot about or did not complete homework assignments and frequently skipped classes. Zeke was more successful with sports. During the last 2 years of high school, he and his friends typically got drunk on Friday and Saturday nights. His drinking led to considerable conflict with his parents who were concerned about him and his influence on his younger brothers.
Zeke completed high school and began a job as a car mechanic. His co-workers were frequent and heavy drinkers. Concurrently, his involvement with sports declined because he was no longer in school and living at home. He began drinking before and during work. He often arrived at work hungover and tired but felt he could ‘‘push through’’ and work effectively. On the night he was charged with impaired driving, Zeke crashed his car into a telephone pole and was arrested for driving while impaired.
Zeke initially did not consider himself as having a substance abuse problem. His father organized a lawyer for the driving charges who recommended a residential treatment program, to which Zeke reluctantly agreed. Although he successfully completed this program, it was years before he successfully embraced abstinence from alcohol.
Perhaps the most striking feature of the epidemiology of alcohol use disorders (AUDs; i.e., alcohol abuse and alcohol dependence) is that these disorders show a marked peak prevalence in late adolescence and early adulthood. Indeed, the remarkable rise and fall in the prevalence of disorder during this life stage suggests that it can be considered, in large part, a developmental disorder of young adulthood (Sher & Gotham, 1999). In this chapter we briefly describe the epidemiology of AUDs (and related alcohol-phenomena) and examine factors that contribute both to their manifestation early in the third decade of life and to their rapid decline soon afterward. We also discuss special considerations in the treatment of young adults with AUDs.
Conceptualizing Alcohol Involvement
Alcohol involvement can be viewed from multiple perspectives. Perhaps the most fundamental distinction concerning an individual’s use of alcohol is whether he or she uses alcohol (i.e., is an abstainer). In the United States, 83% of all individuals over 18 years of age have consumed alcohol, with 70% consuming alcohol in the past year (Grant, Moore, Shepard, & Kaplan, 2003). Most initiation of alcohol use occurs prior (p. 293) to age 21; approximately two-thirds of 18–20 year olds report having consumed alcohol in the past year.
However, there is considerable variation across drinkers in how frequently they consume alcohol and how much they drink when they do consume. Therefore, further distinctions can be made among those who drink with respect to both their frequency and quantity of consumption. Although quantity is assessed as ‘‘typical’’ quantity consumed (in standard drink equivalents), individuals can and often do show high intra-individual variability, leading some researchers to measure volume/variability in order to further resolve an individual’s drinking pattern. These assessments of drinking behavior include a variety of approaches, such as ‘‘graduated frequency’’ approaches (where individuals are queried as to how often they drink varying numbers of drinks/occasions [1–2 drinks, 3–4 drinks, 5–6 drinks, etc.]; Greenfield, 2000) various ‘‘diary’’ approaches (e.g., retrospective, time-line, follow-back assessments; Sobell & Sobell, 2003), and contemporaneous ‘‘diary’’ approaches (Carney, Tennen, Affleck, Del Boca, & Kranzler, 1998; Collins, Kashdan, & Gollnisch, 2003; Perrine, Mundt, Searles, & Lester, 1995) that provide daily (or event-specific) quantities that facilitate more detailed portrayals of drinking patterns. However, such assessments are burdensome and not practicable in many clinical and research contexts.
Consequently, measures of heavy drinking (e.g., drinking five or more drinks in one sitting) are often used to resolve drinking patterns that are likely to be associated with negative consequences. In some approaches, different thresholds are applied to men and women. The 5/4 criteria promoted by Wechsler and Austin (1998) define a ‘‘binge’’ episode as five drinks on an occasion for men and four drinks on an occasion for women. These different thresholds are intended to account for mean sex differences in total body water and first-pass metabolism.
Recently, the National Institute on Alcohol Abuse and Alcoholism (NIAAA; 2004) recommended defining a ‘‘binge’’ episode as drinking that yields a blood alcohol concentration (BAC) of .08% or more, noting the high risk of a number of alcohol-related harms that accompanies BACs at this or higher levels. This document notes that the 5/4 criteria roughly corresponds to a BAC criterion of .08% under a number of restrictive assumptions (e.g., if consumed within 2 hours, if individual is of average BMI and has an average rate of alcohol metabolism). Despite the imprecision of the 5/4 measure (or similar thresholds) for defining binge or heavy drinking, there is increasing recognition that this admittedly crude measure provides a reasonable index of excessive consumption. As we discuss below, young adults often evidence drinking patterns that are characterized by binge drinking.
Alcohol Use Disorders
The fourth edition of the Diagnostic and Statistical Manual (DSM-IV) of the American Psychiatric Association (1994) describes two major forms of alcohol use disorders (AUDs): (1) alcohol abuse, and (2) alcohol dependence, with alcohol dependence presumed to be the more severe disorder and its presence (or its history) excluding the diagnosis of alcohol abuse. In order for an individual to be diagnosed with dependence, three of the following seven criteria must be met: (1) tolerance (marked by either need for increased amounts or diminished effect with same amount of use), (2) withdrawal (defined by the characteristic withdrawal syndrome of the substance or substance is taken to relive/avoid withdrawal symptoms), (3) drinking alcohol in larger amounts or over a longer period than intended, (4) a persistent desire or unsuccessful efforts to cut down or control alcohol use, (5) a great deal of time is spent in obtaining alcohol, using it, or recovering from its effects, (6) giving up important social, occupational, or recreational activities because of alcohol use, and (7) continued use of alcohol despite knowledge that alcohol either caused or exacerbates a persistent or recurrent physical or psychological problem. Alcohol abuse is defined as a maladaptive drinking pattern characterized by one of the following: (1) a failure to fulfill major role obligations at work, school, or home, (2) recurrent alcohol use in situations in which it is physically hazardous, (3) recurrent alcohol-related legal problems, and (4) continued alcohol use despite having persistent or recurrent social or interpersonal problems caused or exacerbated by alcohol.
(p. 294) It should be noted at the present time that many researchers are revisiting the abuse/depen-dence distinction. Mixed abuse and dependence indicators can be well represented by a single factor (e.g., Hasin, Muthuen, Wisnicki, & Grant, 1994). Additionally, item-response theory-based analyses indicate that some dependence criteria (e.g., tolerance, impaired control) are prevalent (and therefore, psychometrically, less severe) and some abuse criteria relatively rare (e.g., legal difficulties) (Kahler, Strong, Hayaki, Ramsey, & Brown, 2003; Saha, Chou, & Grant, 2006). For these reasons, the validity of the category of abuse is increasingly being questioned.
Further, researchers are also reconsidering whether AUDs should be considered as dimensional rather than categorical phenotypes (Hasin, Liu, Alderson, & Grant, 2006; Muthe´n, 2006). Due largely to their ability to meet clinical, health-care planners’, and insurance companies’ needs, categorical diagnosis currently dominates DSM criteria for AUDs (Muthe´n, 2006). However, recent research involving nationally representative data suggests: (1) a continuum, rather than clear boundaries, exists among AUD criteria; (2) new hybrid statistical models that provide both categorical and dimensional representations of AUDs are more suitable than latent class and factor models; and (3) dimensional indicators of AUDs provide more information for research purposes compared to categorical classifications designed for clinical decision making (Hasin, Liu, Alderson, & Grant, 2006; Muthe´n, 2006). These findings are relevant to the abuse/dependence distinction that will likely be reconceputalized in the upcoming DSM-V.
The Epidemiology of Alcohol Use and Alcohol Use Disorders
Over the past 25 years, five large-scale, population-based epidemiological surveys using structured diagnostic interviews have provided estimates of alcohol use disorders in the United States. These include the Epidemiologic Catchment Area (ECA) Survey (Helzer, Burnam, & McEnvoy, 1991; Robins & Price, 1991); the National Comorbidity Survey (NCS; Kessler et al., 1994, 1997); the National Comorbidity Survey – Replication (NCS-R; Kessler, Berglund, Demler, Jin, & Walters, 2005; Kessler, Chiu, Demler, & Walters, 2005); the National Longitudinal Alcohol Epidemiologic Survey (NLAES; Grant, 1997; Grant & Pickering, 1996; Grant et al., 1994); and the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; Grant et al., 2004). Each of these major studies indicates very high past year and lifetime prevalences of AUDs in the U. S. population (13.8% lifetime and 6.8% past year DSM-III in ECA; 23.5% lifetime and 7.7% past year DSM-IIIR in NCS; 18.2 % lifetime and 7.41% past year DSM-IV in NLAES; 30.3% lifetime and 8.46% past year DSM-IV in NESARC; 18.6% lifetime and 4.4% past year DSM-IV in NCS-R).
Figure 18-1 provides representative prevalence data on DSM-IV (past 12 months) alcohol and abuse and dependence from the NESARC. As clearly indicated in this figure, there is a dramatic decline in the prevalence for both alcohol abuse and alcohol dependence in older cohorts, and this pronounced age gradient holds to a large extent across gender and as well as ethnicity (not shown). Additionally, if we subdivide the 18–29 age group into ‘‘emerging adults’’ (18–24) and older young adults (25–29), the age gradient is even more pronounced. This age gradient suggests either a marked developmentally limited condition that tends to remit in the third decade of life or that secular changes occur in the prevalence of AUDs that result in more recently born cohorts having higher prevalences. Comparison of estimates from NLAES (conducted in 1991–1992) and NESARC (2001–2002) reveals an overall increase in the prevalence of alcohol abuse (from 3.03% to 4.65%) and a slight decrease in the prevalence of alcohol dependence (from 4.38% to 3.81%). Of particular interest, the strong age gradient remained and was especially prominent for alcohol dependence, suggesting the age gradient is not an artifact of marked birth cohort differences. Further, large prospective studies of heavy episodic alcohol use in young adulthood (e.g., Chen & Kandel, 1995; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996) show similar patterns and suggest that the age-related decline in prevalence is primarily (p. 295) a developmental phenomenon and is not attributable to secular trends in consumption patterns (Grant, 1997; Sher & Gotham, 1999).
Are Alcohol Use Disorders in Young Adulthood Qualitatively Different Than Later in Life?
The large age gradient raises the question: Is the excess of AUDs seen in young adulthood similar to that found later in life or are they qualitatively different? More than 20 years ago, Zucker (1987; see also 1995) postulated four different subtypes of alcoholism based on developmental course: (1) a developmentally limited form, (2) an antisocial form, (3) a negative affect form, and (4) a developmentally cumulative form. Two of these would be expected to be overrepresented in young adulthood: (1) a developmentally limited form, and (2) an antisocial form. Other theorists (e.g., Babor et al., 1992; Cloninger, 1987; Knight, 1938) posited forms similar to Zucker’s antisocial and negative affect forms, although these subtyping schemes did not clearly articulate the idea of a developmentally limited form of alcoholism. Indeed, the notion that some forms of alcohol dependence might be self-limiting has only recently started to gain recognition, although evidence to this effect has been accumulating for many years (Sher & Gotham, 1999).
Unfortunately, there has been a dearth of prospective studies examining factors that distinguish developmentally limited forms of AUDs from forms that have early onset but persist. The importance of behavioral dysregulation in predicting course of drinking problems has been noted in multiple studies (Zucker et al., 2006). Consistent with Zucker’s taxonomy, Sher, Gotham, & Watson (2004) found that the presence of traits related to antisociality were among the few baseline (measured at age 18) traits that distinguished chronic and remitting (p. 296) courses of AUDs among college students who were initially diagnosed with a DSM-III AUD during the freshman year and were followed up at four additional times over the next 7 years. Although many other variables (e.g., alcohol expectancies, peer norms, and drinking motivations) are robust predictors of excessive consumption and AUDs prospectively (e.g., Baer, 2002), it is less clear how well they distinguish developmentally limited AUDs from more chronic classes when assessed as antecedent or baseline variables. Most of these variables appear to covary with course and are either likely proximal mediators of course or short-term consequences of course (Sher et al., 2004). As we discuss later in the section, The Maturing Out Effect, most of the research targeted at understanding processes associated with the resolution of drinking problems during the third decade of life focuses on developmental factors (especially the assumption of adult role statuses) particular to this stage of life.
One potentially important but understudied question concerns whether young adults who have AUDs show the same type of symptom patterns as older adults with AUDs. In order to address this question we conducted secondary analysis of data from the 2001 to 2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESCARC), a large (n = 43,093) nationally representative sample of the U. S. population. Specifically, we compared the reported alcohol disorder symptoms experienced in the past 12 months of 18–29 year olds diagnosing with an AUD in the past year (abuse or dependence) to individuals 30 and above that also met criteria for past year AUD. Specifically, we examined the frequency of meeting each of the four abuse criteria in past-year alcohol abusers (n = 1,843) stratified by age. In a parallel way, we examined the frequency of meeting each of the seven dependence criteria in past-year alcohol dependent individuals (n = 1,484).
As can be seen in Figure 18-2a, recurrent drinking in situations in which drinking is hazardous (e.g., driving a vehicle while drinking) was the most commonly experienced symptom of alcohol abuse for those individuals meeting criteria for past year alcohol abuse, regardless of age. Younger alcohol abusers (aged 18–29) were less likely to report recurrent drinking in situations in which drinking is hazardous compared (p. 297) to older alcohol abusers aged 30 and above (OR = .30; p < .001), but more likely to experience recurrent alcohol-related legal problems (e.g., get arrested or have other legal problems because of drinking) (OR = 2.96, p<.001) and to report drinking despite social or interpersonal problems caused by drinking (OR = 3.37, p < .001) compared to alcohol abusers over 30.
Profiles of dependence symptoms by age are displayed in Figure 18-2b. Young adults (those 18–29) with alcohol dependence were significantly more likely to experience tolerance symptoms (OR = 1.92, p < .001) and spend much time drinking or recovering from drinking (e.g., spent a lot of time being sick or getting over bad effects of drinking) compared to alcohol dependent individuals over 30 (OR = 1.40, p < .02). In contrast, these young alcohol-dependent individuals were less likely to have the desire or make unsuccessful attempts to reduce or stop drinking (e.g., more than once wanted to stop or cut down on drinking) (OR = .56, p < .001) and less likely to continue using alcohol despite physical or psychological problems caused by drinking (e.g., continue to drink though it was causing a health problem) (OR = .49, p < .02) in comparison to older individuals with alcohol dependence.
In sum, it appears that young adults with AUDs differ in their prevalence of specific alcohol disorder symptoms compared to similarly diagnosed older adults. Perhaps the most striking differences concern the relatively high rates of tolerance and spending much time engaged in drinking or getting over its effects in the younger alcohol dependents. Although one might expect younger alcohol-dependent individuals to report lower levels of tolerance (because tolerance is considered a symptom of neuroa-daptation caused by excessive, chronic use), this finding has also been demonstrated in at least one prospective study (O’Neill & Sher, 2000) and so appears quite robust. The interpretation of this finding is less clear, since it is uncertain whether the strong age gradient is valid or reflects limitations of our ability to assess tolerance with standard interview and question measures (see O’Neill & Sher, 2000, for further discussion of this issue). The finding that younger adults spend more time in procuring, (p. 298) using, and recovering is less surprising given the relatively fewer adult responsibilities that younger adults have, potentially creating a more permissive environment for heavy drinking.
Although the younger alcohol-dependent individuals seem more severely affected than older alcohol dependents with respect to some criteria, they appear less severely affected in others. Most strikingly, younger alcohol-dependent individuals are less likely to drink despite having alcohol-related physical and psychological problems. Thus, older alcohol dependents would be more expected to come to the attention of health providers, which may partially account for the stereotype of the clinical alcoholic as a middle-aged adult with significant medical and psychiatric comorbidity.
In regard to psychiatric comorbidity, existing data regarding the associations between drinking and mood, anxiety, and personality disorders in young adults come largely from clinical samples and, to a lesser extent, general population samples (Dawson, Grant, Stinson, & Chou, 2005). Although individuals with AUDs are at a overall higher risk for a variety of Axis I and Axis II disorders, the relative strength of this association differs as a complex function of not just age but, among younger adults, whether the AUD being considered was abuse or dependence and whether they are college students (see section below). Dawson et al.’s findings are complex but, simply stated, they found that alcohol dependence was associated with extensive comorbidity in both older and younger alcohol-dependent individuals (especially for Axis I disorders and Cluster B Axis II disorders).
Are There Differences in Alcohol Involvement between College Students and Young Adult Non-College Students?
Many young adults are involved in higher education. The highly publicized reports on the rates of heavy drinking on college campuses (Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994) and the alarming estimates of alcohol-related deaths, injuries, and sexual assaults among college students (Hingson, Heeren, Zakocs, Kopstein, & Wechsler, 2002; NIAAA, 2002) have heightened concern about the phenomenon of college student drinking (Slutske et al., 2004). However, until recently it was not clear whether these excessive levels of drinking were specific to college students or more generally characterize alcohol involvement during this stage of life regardless of student status.
Studies examining the differences between high school students who go on to attend college and their non-college-bound peers found that students who go on to attend college were less likely to be heavy drinkers in their senior year of high school than their non-college-attending peers (e.g., Bachman, Wadsworth, O’Malley, Johnston, & Schulenberg, 1997a; O’Malley & Johnston, 2002) but ‘‘catch up’’ to them in drinking after matriculation on some measures of alcohol involvement. Specifically, college students are typically more likely to have consumed alcohol (Crowley, 1991; Gfroerer, Greenblatt, & Wright, 1997; White, Labouvie, & Papadaratsakis, 2005) but are typically found to be as likely or less likely to binge drink compared to their non-college-attending peers (Crowley, 1991; Gfroerer et al., 1997; Muthe´n & Muthe´n, 2000; White et al., 2005). Conversely, several studies have shown that college students were more likely to report heavy drinking than their non-college-attending peers (Hingson et al., 2002; Johnston, O’Malley, & Bachman, 2002; O’Malley & Johnston, 2002). However, the studies that indicate a positive relation between college attendance and binge drinking (e.g., Hingson et al., 2002; Johnston et al., 2002; O’Malley & Johnston, 2002) did not control for background characteristics or living arrangements; when these variables are taken into account, nonsignificant or negative associations between college attendance and problematic drinking are typically found (e.g., Gfroerer et al., 1997; Muthe´n & Muthe´n, 2000; Slutske et al., 2004; White et al., 2005). Furthermore, several studies suggest that AUD prevalences for college students and their non-college-attending peers are equivalent (Dawson, Grant, Stinson, & Chou, 2004; Slutske et al., 2004; Wu, Pilowsky, Schlenger, & Hasin, 2007). In short, early, young adulthood is a time of life characterized by heavy alcohol involvement regardless of whether one is a college student.
(p. 299) However, some correlates of AUDs differ between college students and non-college students. For example, although both alcohol-dependent college students and nonstudent, young adults with alcohol dependence showed extensive comorbidity with a range of Axis I and Axis II conditions in NESARC, comor-bidity appeared lesser in college student alcohol abusers compared to non–student age-abusers (Dawson et al., 2005). This suggests that alcohol abuse reflects less deviance in college students compared to non-students. Dawson and colleagues (2005) speculated that this could reflect norms for heavy drinking among college students, the effects of psychopathology on college matriculation and retention, greater professional and nonprofessional resources to provide various forms of behavioral and emotional support, or a tendency for college students to underutilize alcohol as an emotional regulation strategy.
Additionally, attending college appears to have a salutary effect on alcohol-related outcomes later in adulthood. White et al. (2005) found that college students were more likely to phase out of heavy drinking compared to their nonstudent peers. Though no differences were found in the extent of alcohol-related problems among those who dropped out of high school, graduated high school, or completed college at age 25, Muthe´n & Muthe´n (2000) found that individuals who dropped out of high school reported the highest rates of drinking at age 37, whereas those individuals who went to college reported the lowest rates. Similarly, Harford, Yi, and Hilton (2006), using data from a national survey, found that education beyond high school had a protective effect for alcohol dependence, whereas dropping out of high school increased the long-term risk for alcohol dependence. These findings suggest that college attendance may have a temporary effect of increasing problematic drinking during the college years but have a protective effect on problematic drinking that is manifested later in adulthood.
The finding that consumption increases can be time limited and situational (and not necessarily presage later problems) is important and highlights the fact that drinking during this stage of life appears to be highly reactive to environments that promote drinking, such as campus-based Greek letter organizations (Bartholow, Sher, & Krull, 2002). Greek drinking is associated with selection of heavier drinkers into these environments (McCabe et al., 2005; Park, Sher, & Krull, 2006), but, in turn, the environment further increases drinking. However, the environment-attributable increase in drinking does not show much persistence once an individual leaves the environment (Bartholow et al., 2002).
Related to this is the finding that one’s place of residence during young adulthood (which is confounded with but separable from collegiate status) is associated with large differences in typical drinking patterns in early adulthood. Dawson et al. (2004) found differences between college students and their non-collegiate-age peers were generally smaller than differences within each group according to place of residence. The lowest levels of binge drinking were found among college students living with parents, whereas the highest rates of alcohol abuse were among college students living off campus. The high rates of alcohol abuse among off-campus college students suggest these students are morelikely to engage in impaired driving, the most highly endorsed indicator of abuse (Dawson et al., 2004; see also White et al., 2005). Similarly, Bachman et al. (1997a) found among college students, living in dormitories increased the risk for heavy drinking, whereas living with a spouse decreased the risk.
Overall, both college students and non-students drink heavily. Although there seem to be some difference in drinking patterns between these groups, there is considerably more similarity. Further, the nature of one’s living situation appears to be more important than whether an individual’s primarily role is a student or in the labor market. Thus, as noted previously (e.g., Jackson, Sher, & Park, 2005), the high rates of heavy drinking among college students appear to reflect a ‘‘stage-of-life phenomenon.’’ Perhaps because heavy drinking during early adulthood is relatively normative, treatment seeking in this population is low (see section on Treatment below). However, non-students are more likely to receive treatment for an AUD than college students (Wu et al., 2007).
(p. 300) ‘‘Maturing out’’: What Leads to De-escalation/Desistence of AUDs in Young Adulthood?
The rise in heavy drinking and AUDs during late adolescence and emerging adulthood is attributed, among other factors, to the newfound freedoms associated with leaving the parental home and identity exploration associated with this stage of life. Arnett (2006) refers to emerging adulthood as a ‘‘self-focused’’ time of life, where emerging adults have few social obligations and commitments to others, which grants them a great deal of autonomy in running their own lives. A large majority of these newly autonomous emerging adults who drink perceive coping and social benefits of heavy alcohol use (Schulenberg & Maggs, 2002). However, as the prevalence data indicate, this excess is relatively short lived. What factors then account for the dramatic decline in problematic alcohol use during the third decade of life?
Although there is an extensive literature on ‘‘natural recovery’’ (Sobell et al., 2000) describing the processes whereby many adult, alcohol-dependent individuals recognize a drinking problem and initiate various self-change strategies. However, it seems likely that much of the movement from AUDs to nondisordered drinking occurs in the absence of deliberate self-change efforts (Watson & Sher, 1998). Rather, it appears that much of the reduction in problem alcohol use arises from the natural constraints that traditional adult roles place on an individual’s drinking. Role incompatibility (Yamaguchi & Kandel, 1985) is the theory that the decline of alcohol use (and other substance use) is attributable to the assumption of adult roles such as marriage and parenthood that are ‘‘incompatible’’ with a heavy drinking lifestyle. This theory has gained considerable empirical support in recent years. Emerging adulthood, the period intervening between adolescence and adulthood, represents in American and many other cultures a unique stage of life where the individual is relatively free of the rules and responsibilities related to his or her family of origin but where there is an extended moratorium on taking on adult responsibilities. It is at the beginning of this stage of development when the individual is at highest risk for the onset of an AUD (Grant et al., 2003; Li, Hewitt, & Grant, 2004) and the end of this stage when we begin to see a significant decline in the rate of AUDs.
O’Malley (2004–2005) recently discussed two transitions related to the decline of heavy alcohol use among young adults: marriage and parenthood. Marriage has been shown to reduce alcohol consumption, especially problematic alcohol use (Bachman et al., 1997a; Bachman et al., 2002; Leonard & Rothbard, 1999), whereas remaining single or becoming sepa-rated/divorced is associated with increased alcohol use (Bachman, Wadsworth, O’Malley, Schulenberg, & Johnston, 1997b). Pregnant women appear to reduce their alcohol use for a number of reasons, including the belief that alcohol is harmful to the developing fetus (Coles, 1994). Furthermore, becoming a parent appears to be the key event to prompt men to reduce their drinking (Bachman et al., 1997a). Marriage and parenthood are thought to bring about reductions in social and recreational activities linked to drinking, such as attending parties and going out with friends to bars (O’Malley, 2004–2005).
The relation of social roles and substance use is thought to be underlined by two different processes: role selection and role socialization (Chassin, Presson, Sherman, & Edwards, 1992; Yamaguchi & Kandel, 1985). Role selection refers to the effects of substance use influencing later role involvement, whereas role socialization refers to the influence of adult roles on substance use behaviors. In a study designed to examine the role selection effect of AUDs on later marriage and the role socialization effect of marriage on later AUDs, Gotham, Sher, & Wood (2003) concluded that the role selection effect of AUDs on later marriage is less plausible than the socialization effect of marriage on later AUDs. Furthermore, the authors concluded that role socialization is a major contributor to the maturing-out process of problematic alcohol involvement during emerging adulthood. These findings are consistent with the maturing-out literature discussed by O’Malley (2004–2005).
(p. 301) Psychological Maturation
Though personality traits have traditionally been posited as inherent and static internal dispositions (McCrae et al., 2000), recent perspectives have began to view personality traits as dynamic constructs that make changes across time (e.g., Johnson, Hicks, McGue, & Iacono, 2007; Roberts, Caspi, & Moffitt, 2003). Both cross-sectional and longitudinal studies have documented systematic patterns of mean-level changes in personality traits at various ages across the life span, including during emerging and young adulthood (Caspi, Roberts, & Shiner, 2005; McCrae et al., 1999; Roberts, Walton, & Viechtbauer, 2006). A recent meta-analysis (Roberts et al., 2006) found a clear pattern of normative change across the life course, with people becoming more socially dominant, conscientious, and emotionally stable with age. Consistent with other findings (e.g., Roberts, Caspi, & Moffit, 2001), the study found that personality traits changed more often during emerging and young adulthood than during any other period of the life course, including adolescence.
Further, a normative trend toward personality structures that reflect greater self-control, risk avoidance, agreeableness, and emotional stability as people reach adulthood has been noted by several authors (e.g., Johnson et al., 2007). The tendency for individuals’ personality structures to exhibit developmental adaptation to cope with the roles and tasks associated with adulthood have been labeled as the maturity principle (Caspi et al., 2005). Similar to the maturing-out effect discussed in the extant alcohol literature, these normative changes in personality are largely attributed to individuals undergoing role transitions associated with adulthood, such as parenthood and marriage (Helson, Kwan, John, & Jones, 2002; Roberts, Wood, & Smith, 2005; Roberts et al., 2006). Several empirical studies support this perspective, as changes in femininity and dominance have been linked to marital and family status (Roberts, Helson, & Klohnen, 2002), and emotional stability has been found to correspond with experiencing satisfying relationships (Roberts & Chapman, 2000; Robins, Caspi, & Moffitt, 2002).
Personality traits, especially those related to self-regulation/disinhibition and, to a lesser extent, those related to negative emotionality are robust risk factors for excessive drinking and AUDs. Thus, it seems very plausible that the developmentally normative changes we see in these traits in young adulthood are associated with the ‘‘maturing-out’’ effect. Perhaps because more researchers have assumed personality stability, there is currently a paucity of research addressing this possibility. However, researchers have shown the importance of changes in personality with changes of other important constructs in young adulthood, such as in satisfaction of social roles involving work or relationships (e.g., Roberts et al., 2002; Scollon & Diener, 2006). Thus, it would be somewhat surprising if personality change was not related to changes in drinking-related variables.
Although speculative, another possibility is that neurodevelopment of the prefrontal cortex contributes to the decline of AUDs in young adulthood. Individuals with deficits in executive functioning appear to be at risk for AUDs and other externalizing disorders in general (Giancola, 2000; Giancola & Moss, 1998). The prefrontal cortex undergoes considerable development during adolescence and emerging adulthood and this process is virtually complete by the late 20s (Casey, Galvan, & Hare, 2005; Romine & Reynolds, 2005). Coincident with these neurological changes are further development of cognitive control, self-regulation, and affect regulation (Leibenluft, Charney, & Pine, 2003). These processes are thought to be important in regulating one’s behavior in general and in coping with negative emotions. Thus, although most of the extant maturing-out literature has focused on environmental changes that place individuals in life contexts that constrain drinking, these environmental contexts should be viewed in the broader framework of psychological (i.e., personality and cognitive) maturation.
Treatment of the Young Adult with an Alcohol Use Disorder
Treatment of AUDs in young adults presents special challenges. Ostensibly, the largest challenge is that self-recognition of drinking (p. 302) problems appears to be especially low in this group. For example, analyses conducted in the NESARC database indicate that of individuals with a past 12-month diagnosis of alcohol dependence, only 13% of individuals aged 18–29 reported that they ever sought help for their drinking compared to 37% of individuals aged 30 or over (OR = .25, p < .001). Equally important, only 8% of the younger alcohol-dependent individuals reported that during the past year that they had thought about getting treatment but did not, compared with 19% of similarly diagnosed individuals over 30 (OR = .37, p < .001).
Historically, lack of insight into the nature or extent of one’s own alcohol dependence has been viewed by clinicians as ‘‘alcoholic denial.’’ The exact meaning of the term ‘‘denial’’ varies as a function of theoretical orientation (Sher & Epler, 2004). Psychodynamically oriented clinicians (Freud, 1936/1966) view denial as a type of ego defense. Cognitively oriented clinicians eschew the term ‘‘denial’’ and might prefer to focus on those social-cognitive processes that lead to problem recognition (e.g., discrepancy between internal and external standards of behavior; Nye, Agostinelli, & Smith, 1999) and/or result in social misperceptions (e.g., Prentice & Miller, 1993; Ross, Green, & House, 1977) that facilitate the individual viewing his or her behavior as ‘‘normal.’’ Neurobiologically oriented clinicians might focus on premorbid (Tarter, Alterman, & Edwards, 1984) and alcohol-induced (Duffy, 1995) brain dysfunction that results in limited awareness of one’s deficits (a type of anosog-nosia). Regardless of theoretical perspective, most young adults with alcohol problems do not it.
Unfortunately, many programs utilized in alcohol treatment programs for older adults may not be compatible with the characteristics of young adults. For example, many treatment facilities commonly utilize 12-step programs, such as Alcoholics Anonymous (AA), as a primary treatment component (Mason & Luckey, 2003). Indeed, AA is the most commonly accessed source of help for an alcohol-related problem in the United States (Room & Greenfield, 1993). Although 12-step programs appear to be effective for many (e.g., Vaillant, 2005), predictors of frequent and/or long-term AA attendance, such as being older (Boscarino, 1980; Kolb, Pugh, & Gunderson, 1978) and having more severe histories of alcohol problems (Emrick, Tonigan, Montgomery, & Little, 1993; Vaillant, 1983) are the exact opposite of characteristics found in young adult treatment populations (Mason & Luckey, 2003). In a study comparing differences in characteristics and outcomes in an alcohol treatment sample between young adults aged 18–25 to similarly diagnosed older adults, Mason and Luckey (2003) found that young adults were less likely to attend AA meetings compared to the remainder of the sample. Kelly, Myers, and Brown (2002) found that adolescents who had greater substance-use problem severity were more likely to attend abstinence-focused 12-step groups, although many youths did not affiliate with AA or attended briefly before dropping out. Given that several studies suggest that 12-step approaches have a salutary association with posttreatment substance use among younger age groups (Alford, Koehler, & Leonard, 1991; Hsieh, Hoffmann, & Hollister, 1998; Kelly, Myers, & Brown, 2002), more research is needed to determine what factors may increase the motivation for young adults to attend 12-step programs (Kelly, Myers, & Brown, 2002).
Overall, young individuals who do enter treatment and remain in treatment have relatively favorable outcomes (Grella, Hser, Joshi, & Anglin, 1999). Unfortunately, relative youth has been shown to be a risk factor for dropping out of treatment (Deas, Riggs, Langenbucher, Goldman, & Brown, 2000; Joe, Simpson, & Broome, 1998). Thus, a high priority for treatment research with this population is to focus on the development of treatment programs that young adults will both seek out and remain in. Because of the documented reticence of younger alcohol-dependent individuals to seek treatment, it has been proposed that stepped-care approaches (Institute of Medicine, 1990) that titrate treatment intensity to treatment response may work particularly well with this population. Indeed, initial reports suggest that such stepped-care approaches have high retention rates and participant satisfaction among mandated college student problem drinkers (Borsari, Tevyaw, Barnett, Kahler, & Monti, 2007).
(p. 303) Much of what we know about the treatment of alcohol dependence in young adults comes from the college drinking literature. Although many, if not most, campuses have programs to address problems associated with alcohol, very few of these programs have been validated empirically (Sharmer, 2001; Walters, Bennett, & Noto, 2000). Larimer and Cronce (2007) recently reviewed the literature on individual-focused prevention and treatment approaches for college drinking. Interventions were organized into three broad categories: educational/awareness, cognitive/behavioral skills based, and motiva-tional/feedback based. Education/awareness programs consisted of programs that provided information about problematic drinking (e.g., pamphlets), values clarification, and normative reeducation programs. As also concluded in an earlier review (Larimer & Cronce, 2002), no evidence emerged to support interventions based on information/knowledge alone or interventions based on values clarification. Normative reeducation programs, including personalized normative feedback, produced reductions in drinking and/or consequences, although normative reeducation interventions without personalized normative feedback had less evidence for reducing drinking.
The efficacies of cognitive and behavioral skills-based interventions were generally supported. Motivational/feedback-based interventions were also shown to be efficacious in reducing drinking and drinking-related consequences. Brief motivational interventions with personalized feedback were shown to be efficacious across a variety of treatment modalities (e.g., delivered individually, in groups, or as stand-alone feedback with no in-person contact). Importantly, the efficacy of both brief motivational interventions (e.g., Carey, Carey, Maisto, & Henson, 2006; Marlatt et al., 1998) and skills-based approaches in reducing drinking and consequences of mandated students (e.g., Fromme & Corbin, 2004) appeared promising.
Although the authors cautioned that additional research with stronger research designs and larger sample sizes is needed, they suggested campuses interested in implementing individual-focused prevention programs should consider implementing brief motivational interventions or skills-based programs. Despite the empirical support of the efficacy of skills-based programs and brief motivational interventions in reducing alcohol use and alcohol-related consequences among college students, the overwhelming majority of college students fail to receive treatment for problematic alcohol use (Wu et al., 2007).
Given that heavy consumption is strongly embedded in many critical life contexts in young adulthood, the potential value of harm reduction approaches (Marlatt & Witkiewitz, 2002) has been championed by a number of clinicians and policy makers. Such an approach promotes using behavioral strategies to avoid excessive levels of consumption (e.g., pacing drinking, setting drinking limits) and to minimize harmful consequences when intoxicated (e.g., designated driving programs; Martens et al., 2004). Harm reduction approaches have been shown to be effective and yield clinically significant outcomes in reducing drinking and drinking-related consequences in college students (Baer, Kivlahan, Blume, McKnight, & Marlatt, 2001; Roberts, Neal, Kivlaham, Baer, & Marlatt, 2000). However, critics of harm-reduction approaches warn that that it may increase alcohol use and underage drinking by making it seem acceptable or even accepted and that harm-reduction approaches discourage strict abstinence, which some view as the only acceptable treatment for problematic alcohol involvement (DuPont, 1996). Despite these criticisms, proponents maintain that harm-reduction approaches are not pro-legalization and do not discourage abstinence but rather pragmatically aim to decrease the amount of harm experienced by the individual (Marlatt, Blume, & Parks, 2001). Given that young adults are significantly more interested in reducing their drinking than abstaining, harm-reduction approaches to alcohol use are a compatible and beneficial treatment option for problematic drinkers in young adulthood.
Before concluding this section, it should be emphasized that there are now two medications (naltrexone and acamprosate) that have received FDA approval for the treatment of alcohol dependence (Garbutt, West, Carey, Lohr, & Crews, 1999) and additional promising off-label pharmacotherapies (e.g., (p. 304) topiramate; Johnson et al., 2007) that appear useful in alcohol treatment. Naltrexone, which has been shown to reduce craving in newly abstinent alcohol-dependent individuals (Volpicelli, Alterman, Hayashida, & O’Brien, 1992), may be an especially relevant treatment option for college students, since existing data suggest that it is most useful for early problem drinkers and people with dependence onset before age 25 (Kranzler et al., 2003; Rubio et al., 2005). However, there is a dearth of controlled outcome studies examining effectiveness in young adult treatment populations. This is unfortunate in that there appears to be a small subpopulation of young adults who do not express receptiveness to psychosocial interventions but would consider, indeed prefer, using pharmacotherapy for a drinking problem.
Summary and Future Directions
The peak prevalence and subsequent decline of AUDs that occur over the course of late adolescence and early adulthood suggests AUDs are largely a developmental disorder of young adulthood. Although there is much more similarity than differences in the symptom profiles of young and older individuals with AUDs, analyses included in this chapter reveal young adults who have AUDs differ in their prevalence of specific alcohol disorder symptoms from comparably diagnosed older adults. Most strikingly, younger alcohol-dependent individuals report higher rates of tolerance and spending more time engaged in drinking or getting over its effects and are less likely to drink despite having alcohol-related physical and psychological problems compared to older alcohol dependents. Further, although some differences in alcohol involvement have been found between college attending and non-attending young adults, there appears to be more similarity between these groups. However, attending college appears to have protective effects on alcohol-related outcomes later in adulthood, even though it appears to increase the prevalence of excessive consumption during the college years.
Declines in alcohol involvement toward the end of the third decade of life are usually attributed to individuals attaining adult role statuses incompatible with heavy drinking. Although undoubtedly true, future research should examine the role of psychological maturation (e.g., personality change, neurodevelopment) on the ‘‘maturing out’’ of problematic alcohol involvement given that these psychological processes are changing considerably during this period of time and almost certainly are relevant to understanding health-related behaviors.
Professionals who provide health-related services to young adults are likely to encounter relatively high levels of AUDs in this population. The overall prevalence of excessive alcohol consumption and correlated problems in this population contributes to resistance to the notion that their drinking behaviors are problematic, since many AUDs may be viewed as almost ‘‘normative.’’ Consequently, treatment options that may increase involvement and retention rates, such as motivational interviewing and stepped-care approaches, may be especially important with this group. The appropriate role for pharma-cotherapy in this population requires further investigation but may prove important to the needs of some subgroups of patients. (p. 305)
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