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Journal of Studies on Alcohol and Drugs logoLink to Journal of Studies on Alcohol and Drugs
. 2014 Jul;75(4):684–694. doi: 10.15288/jsad.2014.75.684

Childhood and Adolescent Predictors of Heavy Episodic Drinking and Alcohol Use Disorder at Ages 21 and 33: A Domain-Specific Cumulative Risk Model

Jungeun Olivia Lee a,*, Karl G Hill a, Katarina Guttmannova a, Lacey A Hartigan a, Richard F Catalano a, J David Hawkins a
PMCID: PMC4108607  PMID: 24988267

Abstract

Objective:

Guided by a domain-specific cumulative risk model and an emerging notion of general and alcohol-specific influences, this study examined whether general and alcohol-specific influences from family, peer, and school contexts in childhood and adolescence differentially predict heavy episodic drinking and alcohol use disorder at two developmental periods: the transition to adulthood (age 21) and later in adulthood (age 33).

Method:

Data are from a longitudinal panel study (n = 808) examining the etiology of substance use problems and associated behavior problems from age 10 to age 33 in a Northwest United States urban community sample. The sample is ethnically diverse and evenly distributed by gender (51% male).

Results:

At age 21, alcohol problems were most consistently predicted by adolescent family alcohol and peer alcohol environments and by peer general environment, but not by general family functioning. Conversely, by age 33, alcohol problems were more consistently predicted by general poor family functioning in adolescence and not by family alcohol or any of the peer environment measures.

Conclusions:

Adolescent family and peer alcohol environment influenced alcohol problems at the transition to adulthood. However, alcohol problems later in adulthood were more strongly associated with general poor family functioning in adolescence. These results suggest that alcohol prevention efforts should involve both components designed to reduce alcohol-specific risk and components to improve general family and peer environments during childhood and adolescence.

Young adulthood is a developmental period of particular risk for increased harmful drinking, including heavy episodic drinking and alcohol use disorder. According to the 2011 Monitoring the Future study, about 23% of 12th graders and 37% of college students drank five or more drinks in a row at least once in the 2 weeks before being surveyed (Johnston et al., 2011). In fact, the prevalence of alcohol use disorders is greater among those ages 18–29 than any other adult age group (Grant et al., 2004). This problematic alcohol use presents a serious public health concern both in its own right and through its association with negative consequences, including injuries, hypertension, cirrhosis, and mortality (Corrao et al., 2004; Gmel et al., 2007; Laatikainen et al., 2003; Naimi et al., 2003; Rehm et al., 2001).

The level or severity of drinking behavior has been shown to change across young adulthood. On average, young adults’ harmful drinking reaches its peak at the transition to adulthood (in the early 20s) and then gradually decreases as adulthood progresses (Bachman et al., 2002; Substance Abuse and Mental Health Services Administration [SAMHSA], 2009).

Against these normative trends, longitudinal studies have shown that some young adults suffer from prolonged alcohol problems and may also experience incidental alcohol problems beyond the transition to adulthood (Jackson and Sher, 2005; Jacob et al., 2009; Lee et al., 2012; Schulenberg et al., 1996). Prior studies have emphasized the importance of delineating the age at assessment of adult alcohol problems, particularly at and beyond the normative peak age for problematic drinking, in examining the etiology of adult alcohol problems (Bennett et al., 1999; Merline et al., 2008; Schulenberg and Maggs, 2008; Zucker, 2008)—different predictors might emerge as salient for alcohol problems at the normative peak age versus alcohol problems later in adulthood.

Child and adolescent predictors of adult alcohol problems: Family, peer, and school contexts

Life course (Elder, 1994; McLeod and Almazan, 2003; Shanahan, 2000) and ecological theories (Bronfenbrenner, 2005; Bronfenbrenner and Morris, 1998; Zucker, 2006) suggest that predictors contributing to adult alcohol problems may be implicated across multiple environmental contexts where young adults spend their childhood and adolescence. Existing research has consistently documented that earlier family risk factors indeed predict alcohol problems at varying stages of adulthood. Parental drinking, parental monitoring, and relationship with parents in childhood and/or adolescence predict problematic drinking at the transition to adulthood (Alati et al., 2005; Guo et al., 2001; Hill et al., 2010; Kramer et al., 2008; Merline et al., 2008). Parental drinking has also been shown to predict heavy drinking later in adulthood, in one’s 30s (Merline et al., 2008) and early 40s (Pitkänen et al., 2008). Characteristics of the relationship with one’s parents have been associated with harmful drinking observed beyond the peak age, in one’s early 40s (Maggs et al., 2008).

However, to date, in contrast to studies related to adolescent alcohol problems (for review, see Donovan, 2004; Hawkins et al., 1992; Maggs and Schulenberg, 2004), discussion about the influence of earlier environmental contexts other than family, such as peer and school contexts, on adult alcohol problems is relatively absent with very few exceptions (e.g., Guo et al., 2001; Lee et al., 2012). Guo and colleagues (2001) investigated the effect of each child and adolescent peer, school, and community precursor on alcohol abuse and dependence disorders (based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition [DSM-IV]; American Psychiatric Association, 1994) at the transition to adulthood (age 21). They concluded that factors from earlier environmental contexts other than family, such as bonding to school, significantly predicted alcohol abuse and/or dependence at the transition to adulthood. Lee and colleagues (2012) reported that child and adolescent antisocial and drinking peers influenced the DSM-IV alcohol abuse disorder symptoms at the transition to adulthood (age 21) and later in adulthood (age 33). Although these prior studies shed light on nonfamilial predictors of adult alcohol problems, they evaluated each environmental domain in isolation and did not directly examine unique and/or joint impacts of multiple environmental risk factors on adult alcohol problems, which often operate in unison (Bronfenbrenner, 2005; Moore et al., 2000; Sameroff et al., 1987). Thus, relatively little is known about which predictors are relatively more important than the others or how earlier risk factors and/or environmental contexts might differ in their prediction of adult alcohol problems at the normative peak age (in the early 20s) versus alcohol problems beyond the peak age. Understanding which predictors contribute to alcohol problems at varying points in adulthood would provide important insights for the design of alcohol preventive interventions for youth and young adults.

One difficulty in examining the joint contribution of factors implicated across multiple environmental contexts to the development of alcohol problems lies in managing the sheer number of predictors. Cumulative risk models can be well suited for this task. A cumulative risk modeling strategy assumes that an accumulation of multiple risk factors, rather than any single specific risk factor, negatively affects developmental outcomes (Rutter, 1979; Sameroff, 2000; Sameroff et al., 1987). The cumulative risk model has been widely used in studies examining childhood and adolescent outcomes, including alcohol problems (Appleyard et al., 2005; Evans, 2003; Gerard and Buehler, 2004b; Sameroff et al., 1987; Zufferey et al., 2007). However, most commonly, risks are summed to form a single cumulative risk index without any reference to specific environmental contexts. Although a single summative risk approach certainly has the advantage of parsimony, it incurs a distinct disadvantage: a general, summative cumulative risk index does not provide a clear picture of etiology because it lumps together environmental factors. Consequently, specific implications for the design of effective alcohol prevention approaches can be easily obscured (Ackerman et al., 1999). Reflecting this concern, the present study focuses on three operational environmental domains that have been shown to be influential in the etiology of either adolescent or adult alcohol problems: family (Alati et al., 2005; Guo et al., 2001; Hill et al., 2010; Kramer et al., 2008; Maggs et al., 2008; Merline et al., 2008; Pitkänen et al., 2008), peer (Capaldi et al., 2009; Donovan, 2004; Hawkins et al., 1992; Lee et al., 2012), and school (Botticello, 2009; Catalano et al., 2004; Hawkins et al., 1992) contexts.

Organizing the range of family, peer, and school measures into a few higher order constructs of cumulative risk (referred to as a domain-specific cumulative risk model, below) permits consideration of multiple aspects of these environmental domains while minimizing the number of statistical tests needed. This domain-specific cumulative risk model is useful because (a) it provides a concrete, easily grasped picture of the environment and (b) it provides a conceptualization of the environment that is more refined than just general cumulative risk but not so detailed as to be unmanageable in multivariate analyses.

Integrating the domain-specific cumulative risk model and general and alcohol-specific influences

An emerging understanding in the literature on environmental risk factors associated with alcohol problems underscores the importance of another crucial dimension: general versus outcome-specific risk factors (Capaldi et al., 2008, 2009; Duncan et al., 2006; Moffitt, 1993). General environmental risk factors concern a broad developmental setting for individuals (e.g., an adverse family-rearing context), whereas specific environmental risk factors are properties related to a specific outcome of interest (e.g., parents involved in heavy episodic drinking). Moffitt (1993) posited that such general and outcome-specific environmental risk factors might have differential associations with problematic behavior persistent beyond the normative peak age versus its developmentally limited version. More specifically, it was hypothesized that exposure to a general negative family environment (e.g., an adverse family-rearing context) contributes to problematic behavior persistent beyond its normative peak age (life-course-persistent antisocial problems), whereas an outcome-specific peer environment (e.g., peers’ antisocial behavior) is predictive of its developmentally confined version (adolescent-limited antisocial problems).

Prior empirical work related to adolescent drinking has documented that general and alcohol-specific environmental risk factors indeed have different associations with adolescent alcohol problems in varying stages of adolescence (Capaldi et al., 2009; Duncan et al., 2006). For example, Capaldi and colleagues (2009) showed that the general peer environment (e.g., exposure to deviant peers) predicts alcohol use only in late adolescence, whereas the alcohol-specific peer environment (e.g., associating with drinking peers) predicts both early and later adolescent alcohol use. A prior study has also documented differential associations of general and alcohol-specific risk factors with adult problematic drinking in varying stages of adulthood.

Lee et al. (2012) concluded that a general negative peer environment in adolescence (e.g., exposure to deviant peers) is an important risk factor for adult alcohol problems later in adulthood, in one’s early 30s, whereas an alcohol-specific adolescent peer environment (e.g., having drinking peers) influences alcohol problems at the transition to adulthood, primarily in one’s early 20s. By building on these insights of general and outcome-specific environmental influences and relevant prior empirical studies, the present study conceptually integrates the domain-specific cumulative risk model and a notion of general and alcohol-specific influences. Guided by the integrated conceptual model, the current study articulates the domain-specific cumulative risk indices into general and alcohol-specific cumulative risk indices for each of the three environmental domains, resulting in six measures of cumulative risk in the child and adolescent environments: general and alcohol-specific family, peer, and school environments.

Present study

Using a prospective longitudinal design, the present study sought to evaluate the contribution of child and adolescent risk factors experienced in the family, peer, and school contexts to adult alcohol problems at the transition to adulthood (age 21) and beyond the normative peak age (age 33). The identified risk factors were organized into a few higher order constructs of general and alcohol-specific cumulative risk factors with reference to each context (e.g., general family cumulative risk and alcohol-specific family cumulative risk), permitting consideration of multiple aspects of these environmental domains while minimizing the number of statistical tests needed. The central research question addressed is: Do general and alcohol-specific influences in family, peer, and school contexts differentially predict alcohol problems at the transition to adulthood (age 21) and beyond the normative peak age (age 33)?

Relying on notions of general and outcome-specific risk factors in Moffitt’s theoretical conceptualization (Moffitt, 1993) and relevant prior empirical studies (Capaldi et al., 2009; Duncan et al., 2006; Lee et al., 2012), we hypothesized that different environmental domains and their general and alcohol-specific influences might gain or lose their salience for alcohol problems at age 33, compared with alcohol problems at age 21. More specifically, we hypothesized that family domain, particularly general family domain, would emerge to be the most consistent predictor of alcohol problems later in adulthood (i.e., alcohol problems at age 33). In contrast, peer domain—particularly its alcohol-specific influence—would emerge to be the most salient predictor of alcohol problems at the transition to adulthood (i.e., alcohol problems at age 21), when these problems are more normative.

A conceptual model illustrating these questions is presented in Figure 1.

Figure 1.

Figure 1

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Conceptual model illustrating the predictors and outcomes examined in regression analyses

Method

Sample

Data are from the Seattle Social Development Project (SSDP), a longitudinal study examining the etiology of substance use problems and associated behavior problems from ages 10 to 33. In the fall of 1985, all fifth-grade students (N = 1,053) in 18 Seattle elementary schools that oversampled children from high-crime neighborhoods were recruited for the study. Of these, 808 students (77%) agreed to be a part of the SSDP sample. Analyses presented here include data collected during late childhood (age 10), adolescence (ages 11–18), young adulthood (age 21), and adulthood (age 33). All study procedures were approved by the Human Subjects Review Committee of the University of Washington.

The sample is evenly distributed across two genders (51% male) and includes multiple ethnic groups (47% European American, 26% African American, 22% Asian American, and 5% Native American). About half (52%) were low-income families as indicated by eligibility for the National School Lunch/School Breakfast Program between ages 10 and 13. Participation rates of the sample have remained high; of those still living (23 participants were known to be deceased by age 33), 92% (n = 721) of respondents participated in the age 33 interview.

Measures

Outcomes: Alcohol problems at the transition to adulthood (age 21) and beyond the normative peak age (age 33)

  • (a) Heavy episodic drinking: Heavy episodic drinking was assessed by participants’ self-report of the number of times they had five or more drinks in a row of any type of alcoholic beverage in the past month at ages 21 and 33. For the present analysis, those having five or more drinks at least once were assigned 1 at each age and 0 otherwise.

  • (b) Alcohol use disorder: Alcohol use disorder at ages 21 and 33 was assessed through the Diagnostic Interview Schedule (DIS) (Robins et al., 1981). Measures of alcohol use disorder symptoms consist of 11 symptoms (four symptoms for alcohol abuse disorder and seven symptoms for alcohol dependence disorder), including, for example, failure to fulfill major role obligations, recurrent alcohol-related legal problems, development of tolerance to alcohol, experiencing withdrawal symptoms, increased intake amount, and spending a great deal of time on activities necessary to obtain alcohol. Those who met either the DSM-IV alcohol abuse diagnostic threshold (American Psychiatric Association, 1994) or the DSM-IV alcohol dependence diagnostic threshold (American Psychiatric Association, 1994) were assigned 1 at each age (21 and 33) and 0 otherwise.

(c) Risk factors in family, peer, and school contexts (ages 10–18): Table 1 shows a summary of the six environmental predictors and the risk and protective factors from which they were developed. Multiple constructs in family, peer, and school contexts were identified using as guidelines the ecological perspective (Bronfenbrenner, 2005), the life course perspective (Elder, 1994), and the literature review on this topic. In general, a similar set of items was used to measure each risk factor across all ages. When possible, multiple items were used to assess each construct at each age in each environmental domain, and all items were standardized to ensure a common metric and equal weight across items. The standardized items were averaged into a scale score for each construct at each age and then averaged across ages into a single composite measure representing each construct. In line with the cumulative risk literature (Appleyard et al., 2005; Gerard and Buehler, 2004a), these composite scales were dichotomized by assigning a value of 1 to those in the riskiest quartile in the distribution of each construct. Guided by the domain-specific cumulative risk model and an emerging notion of general and alcohol-specific influences, the resultant dichotomies were summed into the six general and alcohol-specific cumulative risk indices.

Table 1.

General and alcohol-specific risk factors in childhood and adolescent family, peer, and school contexts (ages 10–18)

Construct No. of items (across ages) Reliabilities, (across ages) Example items
Family
 General
  Family management 41 .83 When you are away from home, do your parents know where you are and who you are with?
  Family conflict 23 .81 Often people in my family yell at each other.
  Family involvement 61 .71 On weekdays, how many meals does your family eat together each day?
  Family bonding 40 .81 Would you like to be like your father/mother?
 Alcohol specific
  Parental drinking attitudes 18 .81 How much do you [parent] think people risk harming themselves, physically or in other ways, if they drink alcohol occasionally?
  Respondent’s involvement with parents’ drinking 4 .81 Has [student] ever brought, opened, or poured a drink containing alcohol for a family member?
  Parental heavy episodic drinking 8 .77 When you do drink, how often do you have as many as five or six drinks (at one time)?
  Sibling drinking 4 .77 During the past year, how many of your brothers and sisters used alcohol without the permission of your parents?
Peer
 General
  Antisocial peers 53 .77 Have your friends done anything that could get them into trouble with the police?
  Antisocial opportunities 28 .81 Have you ever been invited to join a gang?
 Alcohol specific
  Drinking peer 25 .81 Has friend #1 tried beer, wine, or distilled spirits when their parents didn’t know about it?
School
 General
  Bonding 36 .78 Most mornings I look forward to going to school.
  Opportunities 46 .73 Even students who don’t do well in school help decide things like class activities and rules.
  Involvement 20 .79 I take part in class discussions and activities
  Rewards 57 .77 The school lets my parents know when I have done something well.
 Alcohol specific
  Perception of drinking in school 9 .79 Most people in my school think it’s ok for people my age to drink alcohol.
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Note: No. = number.

On one hand, concerns with dichotomizing variables have been noted in prior literature, including a potential decrease in statistical power and the loss of information (Cohen, 1983; Dinero, 1996). On the other hand, others have argued that the practice of dichotomization may not necessarily result in such statistical compromise. For example, Farrington and Loeber (2000) examined whether dichotomization caused the problems noted above and concluded that dichotomous variables, when appropriately analyzed, provide reliable, meaningful, and easily digestible results. In particular, they have highlighted the utility of dichotomization in studies examining a phenomenon that does not inherently follow a normal distribution because of its rarity of occurrence. They have also underscored that dichotomization synchronizes very well with a “risk factor” approach, which facilitates intervention efforts. Given that outcomes in the present study are not expected to follow a normal distribution and a cumulative risk model is well suited for the purpose of this study, we decided to dichotomize predictors.

Participant gender (1 = male), early alcohol use (ages 10–11), and early behavioral disinhibition (age 14) (Carver and White, 1994; Iacono et al., 1999) were included as covariates in each model. In addition, for age 33 alcohol outcomes, age 21 alcohol problems were also included as covariates.

Analysis strategy

The analysis strategy was divided into two parts. First, to examine the effects of each general and alcohol-specific risk factor in childhood and adolescence on heavy episodic drinking and alcohol use disorder in adulthood, we estimated a series of logistic regressions examining effects of each risk factor, independently from the others, on the two separate alcohol problem measures at ages 21 and 33. Second, multivariate logistic regression analyses were estimated to evaluate joint and unique influences of each environmental domain and effects of general and alcohol-specific cumulative risk factors on two separate alcohol problem measures at ages 21 and 33. For all analyses, missing data were managed via multiple imputation (Acock, 2005; Buhi et al., 2008; Schafer and Graham, 2002). Forty imputed data sets were created using ICE (Imputation by Chained Equations) in STATA 12 (StataCorp LP, College Station, TX). The imputation models included all the main analysis variables as well as covariates. All regression parameters and standard errors for the main analyses were combined using the MI function in STATA 12.

Results

Prevalence of adult alcohol problems

At age 21, 31.8% of the sample had five or more drinks in a row at least once in the past month, and 26.6% of the sample met alcohol abuse diagnostic criteria or alcohol dependence diagnostic criteria. At age 33, 23.4% of the sample reported involvement in heavy episodic drinking; 15.2% of the sample met alcohol abuse diagnostic criteria or alcohol dependence diagnostic criteria. The pattern of results across ages shows that, on average, problem drinking decreased as participants got older, consistent with nationally representative samples (Bachman et al., 2002; SAMHSA, 2009).

Childhood and adolescent family, peer, and school general and alcohol-specific risk factors predicting adult alcohol problems

Table 2 summarizes a series of logistic regressions examining the effects of each general and alcohol-specific risk factor on each type of adult problematic drinking. The first four data columns present the odds ratios for each risk factor, taken singly, predicting age 21 heavy episodic drinking and alcohol use disorder, respectively. The next four columns present the odds ratios for each risk factor, taken singly, predicting age 33 heavy episodic drinking and alcohol use disorder, respectively. The odd-numbered columns present the odds ratios for each risk factor without any covariate. The even-numbered columns present the adjusted odds ratios for each risk factor, controlling for age 10 to age 11 alcohol use, age 14 behavioral disinhibition, and gender. For the age 33 alcohol problems, the corresponding age 21 problem alcohol involvement was also added as an additional covariate (e.g., for the model predicting heavy episodic drinking at age 33, heavy episodic drinking at age 21 was added as another covariate).

Table 2.

Odds ratios predicting age 21 and age 33 heavy episodic drinking (HED) and alcohol use disorder (AUD) associated with adolescent risk factors

Variable Age 21a
 Age 33b
HED
 AUD
 HED
 AUD
1. Zero-order 2. w/control 3. Zero-order 4. w/control 5. Zero-order 6. w/control 7. Zero-order 8. w/control
Family
 General
  Family management 0.92 0.76 1.42 1.19 1.47 1.36 2.15 1.88
  Family conflict 1.03 0.97 1.20 1.14 1.48 1.68 1.61 1.58
  Family involvement 0.76 0.64 0.99 0.85 1.40 1.36 1.66 1.57
  Family bonding 1.00 0.86 1.49 1.33 1.50 1.39 1.72 1.54
 Alcohol specific
  Parental drinking attitude 1.57 1.46 1.35 1.24 1.57 1.28 1.33 1.20
  Respondents’ involvement with parents’ drinking 2.15 2.17 1.27 1.24 1.35 1.13 1.57 1.58
  Parental heavy episodic drinking 1.73 1.68 1.66 1.60 1.32 1.14 1.67 1.49
  Sibling drinking 1.67 1.59 1.64 1.51 1.25 1.08 1.31 1.05
Peer
 General
  Antisocial peers 1.65 1.14 2.74 2.06 1.43 0.88 2.20 1.38
  Antisocial opportunities 1.30 0.95 2.42 1.91 1.51 1.17 2.60 1.81
 Alcohol specific
  Drinking peer 2.55 2.55 2.59 2.50 1.16 0.81 2.26 1.82
School
 General
  Bonding 1.03 0.83 1.68 1.39 1.27 1.13 2.35 1.91
  Opportunity 1.18 0.94 1.57 1.27 1.63 1.39 2.41 1.99
  Involvement 0.79 0.73 1.09 1.02 0.95 0.94 1.23 1.15
  Reward 1.46 1.31 1.43 1.27 1.14 0.97 1.68 1.47
 Alcohol specific
  Perception of drinking in school 1.18 1.13 1.41 1.34 1.22 1.39 2.01 1.92
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Notes: Coefficients in bold are significant at p < .05; coefficients in italics are marginally significant at p < .10.

a

Early alcohol use (ages 10–11), behavioral disinhibition (age 14), and gender were included in the models as covariates;

b

early alcohol use (ages 10–11), behavioral disinhibition (age 14), gender, and the corresponding age 21 problem alcohol involvement (e.g., for the model predicting HED at age 33, HED at age 21 was added as a covariate) were included in the models as covariates.

Overall, measures of general adolescent family functioning were more consistently predictive of alcohol problems at age 33 than they were at age 21. Poor family management, high family conflict, low family involvement, and low family bonding all predicted some degree of age 33 alcohol problems. By contrast, measures of alcohol-specific adolescent family environment, such as parental heavy episodic drinking and sibling drinking, were more consistently associated with age 21 alcohol problems than they were at age 33. Adolescent peer influences, particularly alcohol-specific measures, were more consistently predictive of alcohol problems at age 21 than they were at age 33. Interestingly, both general and alcohol-specific adolescent school factors were more consistently predictive of alcohol problems at age 33 than they were at age 21.

Importantly, contextual risk factors do not operate in isolation. Thus, the next set of analyses examined how general and alcohol-specific influences in multiple environmental contexts uniquely and/or jointly operated to influence adult alcohol problems and whether such general and alcohol-specific influences in multiple environmental contexts gain or lose their salience for alcohol problems at age 33, compared with alcohol problems at age 21.

To examine these issues, adolescent general and alcohol-specific cumulative risk indices from family, peer, and school contexts were entered into multivariate logistic regressions as predictors of heavy episodic drinking and alcohol use disorder at ages 21 and 33, respectively. All the covariates were also added. Tables 3 and 4 present multivariate logistic regression results.

Table 3.

Multivariate logistic regressions predicting heavy episodic drinking (HED) associated with general and alcohol-specific cumulative risk factors in adolescence (ages 10–18), odds ratio, and 95% confidence interval

Variable Age 21
 Age 33
Family Peer School Full model Family Peer School Full model
Family
 General 0.89 [0.78, 1.01] 0.88 [0.76, 1.02] 1.19 [1.02, 1.39] 1.22 [1.03, 1.44]
 Alcohol specific 1.54 [1.30, 1.82] 1.45 [1.22, 1.72] 1.13 [0.92, 1.38] 1.16 [0.94, 1.43]
Peer
 General 0.88 [0.69, 1.11] 0.92 [0.72, 1.18] 1.05 [0.81, 1.36] 0.95 [0.72, 1.25]
 Alcohol specific 2.71 [1.80, 4.10] 2.37 [1.53, 3.65] 0.80 [0.48, 1.31] 0.67 [0.40, 1.12]
School
 General 0.96 [0.83, 1.10] 0.97 [0.83, 1.14] 1.03 [0.88, 1.22] 0.97 [0.81, 1.16]
 Alcohol specific 1.15 [0.78, 1.71] 0.93 [0.61, 1.43] 1.37 [0.86, 2.16] 1.46 [0.90, 2.37]
Early alcohol use (ages 10–11) 1.01 [0.79, 1.29] 0.96 [0.75, 1.22] 1.05 [0.83, 1.33] 0.95 [0.74, 1.22] 0.95 [0.72, 1.25] 1.02 [0.77, 1.35] 0.99 [0.75, 1.30] 0.98 [0.73, 1.30]
Behavior disinhibition (age 14) 1.22 [1.03, 1.45] 1.11 [0.93, 1.34] 1.26 [1.06, 1.50] 1.12 [0.92, 1.35] 1.08 [0.90, 1.31] 1.17 [0.95, 1.44] 1.09 [0.89, 1.33] 1.12 [0.90, 1.39]
Gender (Male =1) 2.51 [1.80, 3.48] 2.74 [1.96, 3.82] 2.47 [1.78, 3.42] 2.76 [1.95, 3.92] 3.64 [2.42, 5.46] 3.47 [2.30, 5.23] 3.81 [2.51, 5.77] 3.78 [2.46, 5.79]
HED (age 21) 2.91 [1.98, 4.28] 3.01 [2.06, 4.41] 2.94 [2.02, 4.28] 3.06 [2.06, 4.54]
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Notes: Coefficients in bold are significant at p < .05; coefficients in italics are marginally significant at p < .10.

Table 4.

Multivariate logistic regressions predicting alcohol use disorder (AUD) associated with general and alcohol-specific cumulative risk factors in adolescence (ages 10–18), odds ratio, and 95% confidence interval

Variable Age 21
 Age 33
Family Peer School Full model Family Peer School Full model
Family
 General 1.05 [0.92, 1.20] 0.98 [0.84, 1.14] 1.27 [1.07, 1.50] 1.17 [0.97, 1.41]
 Alcohol specific 1.30 [1.10, 1.54] 1.20 [1.01, 1.44] 1.25 [0.99, 1.58] 1.20 [0.94, 1.53]
Peer
 General 1.43 [1.13, 1.82] 1.43 [1.12, 1.84] 1.28 [0.95, 1.73] 1.11 [0.81, 1.52]
 Alcohol specific 2.12 [1.38, 3.26] 1.94 [1.24, 3.05] 1.63 [0.94, 2.80] 1.29 [0.72, 2.33]
School
 General 1.10 [0.95, 1.27] 1.04 [0.88, 1.22] 1.24 [1.04, 1.49] 1.14 [0.94, 1.39]
 Alcohol specific 1.28 [0.85, 1.93] 0.94 [0.61, 1.46] 1.74 [1.05, 2.88] 1.53 [0.90, 2.60]
Early alcohol use (ages 10–11) 0.90 [0.69, 1.16] 0.84 [0.65, 1.09] 0.93 [0.72, 1.19] 0.83 [0.64, 1.08] 0.63 [0.42, 0.96] 0.65 [0.43, 0.97] 0.68 [0.46, 1.0] 0.61 [0.40, 0.93]
Behavior disinhibition (age 14) 1.29 [1.10, 1.53] 1.08 [0.90, 1.31] 1.28 [1.07, 1.52] 1.07 [0.88, 1.30] 1.23 [0.99, 1.53] 1.16 [0.92, 1.46] 1.17 [0.93, 1.46] 1.06 [0.83, 1.36]
Gender (male =1) 2.43 [1.72, 3.43] 2.50 [1.75, 3.57] 2.49 [1.76, 3.54] 2.46 [1.71, 3.55] 1.91 [1.21, 3.01] 1.92 [1.20, 3.06] 2.07 [1.30, 3.32] 2.10 [1.29, 3.42]
AUD (age 21) 2.59 [1.63, 4.11] 2.39 [1.50, 3.83] 2.63 [1.66, 4.17] 2.40 [1.49, 3.86]
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Notes: Coefficients in bold are significant at p < .05; coefficients in italics are marginally significant at p < .10.

As shown in the full-model columns of Tables 3 and 4, a cumulative risk measure of general family functioning was more consistently predictive of age 33 alcohol problems than it was at age 21. By contrast, a cumulative risk measure of family alcohol-specific environment more consistently predicted age 21 alcohol problems. Cumulative indices of peer environment were also consistently predictive of the age 21 alcohol problems, whereas neither general nor alcohol-specific peer measures were predictive of age 33 alcohol problems. Interestingly, the statistically significant effects of school general and alcohol-specific cumulative risk indices on young adults’ alcohol problems seen in the bivariate models were accounted for by either family or peer measures.

Discussion

The present study examined the association between general and alcohol-specific risk factors arising from multiple environmental contexts where young adults spend their childhood and adolescence and adult alcohol problems at two developmental periods: the transition to adulthood and later in adulthood. Guided by life course and ecological perspectives and the current literature on this topic, this study identified multiple general and alcohol-specific risk factors from family, peer, and school contexts in childhood and adolescence. By building on the domain-specific cumulative risk model and an emerging notion of general and alcohol-specific influences, the identified risk factors were conceptualized to operate in a domain-specific cumulative manner. Furthermore, we hypothesized that different environmental contexts and general and alcohol-specific influences may gain or lose their salience for alcohol problems later in adulthood, compared with alcohol problems at the transition to adulthood, when these problems are more normative.

Results from this study indicate that each general and alcohol-specific risk factor from family, peer, and school contexts in childhood and adolescence may indeed contribute to adult alcohol problems. Importantly, multivariate results from this study clearly show that environmental contexts and general and alcohol-specific influences were differentially associated with the young adult and later adulthood alcohol problems. Peer cumulative risk indices and alcohol-specific influences in childhood and adolescence predicted alcohol problems in the transition to adulthood. In particular, the peer alcohol-specific measure was consistently associated with both adult alcohol outcomes at this developmental period, suggesting that exposure to adolescent drinking peers is a robust predictor of alcohol problems at the transition to adulthood. The alcohol-specific family measure was also predictive of heavy episodic drinking and alcohol use disorder in the transition to adulthood. In contrast, only negative general family environment in childhood and adolescence contributed to alcohol problems later in adulthood, confirming the salience of this specific social influence on alcohol problems for the later part of young adulthood.

These study findings shed light on the “tug-of-war” debate (Newcomb, 1992) over the relative influences of family and peers on substance use problems (Aseltine, 1995; Hoffmann, 1993). Coupled with prior studies concluding that the influences from family context on problematic substance use are weak during adolescence in the presence of peer influences (Aseltine, 1995; Bahr et al., 1998; Brook et al., 2001; Capaldi et al., 2009; Duncan et al., 2006), our study suggests that the primacy of family influences may temporarily decrease during adolescence but may be regained as individuals get older. Our research findings also echo Moffitt’s (1993) conceptual perspective on the etiology of life-course-persistent and adolescent-limited antisocial behavior and are in line with prior work on adult alcohol problems (Lee et al., 2012), confirming the importance of the general influences on adult alcohol problems persistent beyond the normative peak age.

Prior research on adolescents’ alcohol use has indicated that the alcohol-specific risk factor was consistently associated with alcohol use in late adolescence, whereas the general risk factor was not (Capaldi et al., 2009; Duncan et al., 2006). Reflecting this, our study suggests that the relative importance of general and alcohol-specific influences of environmental contexts on alcohol problems may also change as a function of developmental stages. More specifically, our study indicates that the general influences of environmental contexts on problematic drinking may gain their salience as one gets older. These findings caution against interventions narrowly focusing on alcohol-specific risk factors (e.g., viewing parents’ alcoholism as the defining risk factor for alcohol problems among the children of alcoholics). Rather, the present study strongly supports the translation of general aspects of environmental contexts, particularly family context in childhood and adolescence, into prevention programs to curb the occurrence of and to promote the desistence from problematic alcohol involvement among young adults.

Interestingly, multivariate longitudinal analyses indicated that the statistically significant effect of school general and alcohol-specific cumulative risk indices on adult alcohol problems was accounted for by either family or peer measures. In the analyses where each risk factor was evaluated independently from the other factors, weak bonding to school and a lack of prosocial opportunities at school were associated with clinically significant alcohol problems later in adulthood, even after adjusting for respondents’ earlier alcohol use. Also, the school general and alcohol-specific cumulative risk indices were predictive of alcohol use disorder at age 33 in the analyses where family and peer cumulative risk measures were excluded. One possibility is that the effect of school factors may be influenced by family factors. Prior studies have reported that the family may shape a child’s broader environmental contexts, including school (Gutman and McLoyd, 2000; Parcel et al., 2010). Examining potential paths among these early environmental domains could be fruitful in future studies of the mechanisms leading to adult alcohol problems.

These findings should be interpreted in the context of study limitations. First, all the measures were derived from respondents’ self-reports. Results from respondents’ reported perceptions of their environmental contexts and drinking behavior may differ from other ways of assessing environments and outcomes. Second, the peer alcohol-specific environment and the school alcohol-specific environment were each represented with a single construct; however, these measures did show predictive capacity, as shown throughout the analyses. Third, the study sample is not nationally representative, and generalization of findings should be carried out with caution.

The current study makes two significant contributions to the literature. First, by capitalizing on prospective longitudinal data spanning late childhood, adolescence, the transition to adulthood, and adulthood, the study examines the general and alcohol-specific influences of risk factors arising from not only earlier family context but also earlier peer and school contexts on adult alcohol problems, including clinically significant alcohol problems. Second, the current study contributes to the literature by conceptually articulating cumulative risk modeling from a perspective of prevention efforts (referred to as a domain-specific cumulative risk model in the current study) and integrating the domain-specific cumulative risk model and an emerging notion of general and alcohol-specific influences. By building on the integrated conceptual model, we then hypothesized and investigated differential impacts of general and alcohol-specific cumulative measures across multiple environmental contexts in adolescence on alcohol problems at the transition to adulthood versus alcohol problems beyond the normative peak age.

In taking this approach, the study found that alcohol problems at the transition to adulthood were influenced by earlier peer influences, in particular being affiliated with drinking peers; however, alcohol problems later in adulthood were influenced by negative general family environment (which had not been predictive earlier). These findings suggest that adult alcohol problems indeed stem from one’s childhood and adolescent experience in not only peer context but also family context. Hence, our research suggests that preventing the emergence and persistence of adult alcohol problems can and should begin early. Our research also suggests that family context, along with peer context, should be considered as an important avenue for prevention programs to reduce alcohol problems among young adults. Study findings also suggest that not only alcohol-specific risk factors but also general negative risk factors, and general family influences in particular, are associated with alcohol problems later in adulthood, indicating that prevention efforts narrowly targeted at adolescents’ affiliation with their drinking peers may not be effective in reducing alcohol problems later in adulthood. Our research suggests that family-based interventions improving parental monitoring and facilitating meaningful family interaction in childhood and adolescence may help promote desistence from problematic drinking behavior among young adults. Prevention efforts that involve a broad spectrum of program components designed to reduce both general and alcohol-specific risk factors across multiple environmental contexts, particularly family and peer domains, are needed to discourage the development and persistence of alcohol problems in adulthood.

Footnotes

This work was supported by National Institute on Alcohol Abuse and Alcoholism Grant R01 AA 016960; National Institute on Drug Abuse Grants R01 DA 003721-01-08, R01 DA 09679-01-14, and R01 DA 024411-01-02; and Robert Wood Johnson Foundation Grant 21548. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. Richard F. Catalano is a board member of Channing Bete Company (South Deerfield, MA), distributor of Supporting School Success and Guiding Good Choices. Although the intervention effects are not studied in this article, these programs were tested in the study that produced the data set used in this article.

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