Understanding Alcohol Consumption and Its Correlates among African American Youths in Public Housing: A Test of Problem Behavior Theory Margaret Lombe, Mansoo Yu, Von Nebbitt, and Tara Earl African American youths are overrepresented in urban public housing developments characterized by violence, poverty, and alternative market activities. Using Jessor and Jessor’s problem behavior theory (PBT), the authors examined alcohol use and its correlates in a sample of African American youths from three public housing developments (N = 403). Multiple logistic regression analyses were performed to estimate the relative contributions of demographics, personality, environment, and behavior system variables in predicting past-year alcohol use. Results provide support for PBT. Depressive effects and causes were significant predictors of adolescent alcohol use. Delinquent behavior and affiliation with delinquent peers were also associated with alcohol use. Furthermore, age was related to alcohol use. Implications for practice and future inquiry are suggested. Key words: African American
youths; alcohol use; disaggregated effects; problem behavior theory; public housing development
A
frican American youths are overrepresented in urban public housing developments characterized by violence, poverty, and alternative market activities. In the past few years, research has begun to focus specifically on this vulnerable population of youths. This research has contributed to our understanding of how various domains relate to African American adolescents’ symptoms and behaviors, especially in the context of public housing neighborhoods. Alcohol use or misuse is a common maladaptive behavior in the United States (U.S. Department of Health and Human Services, 2001). Research evidence shows that problems associated with excessive alcohol consumption—for example, liver disease, cardiovascular disease, neurological damage, and psychiatric problems—tend to be exacerbated in youths reporting early initiation (Odgers et al., 2008). In particular, evidence suggests that alcohol is the most widely used drug by youths (Office of Juvenile Justice and Delinquency Prevention [OJJDP], 2008). African American youths drink less than other youths on average (Substance Abuse and Mental Health Services Administration [SAM-
CCC Code: 1070-5309/11 $3.00 and ©2011 National Association Social Lombe, Yu, Nebbitt, Earl / Alcohol Use of and Its Workers Correlates
HSA], 2009). However, results of national surveys reveal that while frequent heavy drinking among white male individuals ages 18 to 29 continued to drop, rates of heavy drinking and alcohol-related problems remained high among African Americans in the same age group (see, for example, SAMHSA, 2009). Consequently, the age-adjusted death rate from alcohol-related diseases for African Americans is 10% greater than it is for the general population (Kochanek, Murphy, Anderson, & Scott, 2004). Furthermore, alcohol use is related to the four leading causes of death among African Americans ages 12 to 20: homicide, unintentional injuries, car accidents, and suicide (National Center for Injury Prevention and Control, 2006). In this group, alcohol consumption has also been linked to depressive symptoms (Grant, 1997). Other consequences of alcohol use or misuse include later adolescent predicaments, inhibiting acquisition of skills necessary for employment, and heightened health risks. We used Jessor and Jessor’s (1977) problem behavior theory to help understand and explicate adolescent alcohol use in a sample of African American youths in public housing.The conceptual structure of the theory consists of three major systems that
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explain problem behavior: personality, perceived environment, and the behavior system. The theory asserts that each system serves either as instigator for or control against engaging in problem behavior. Proneness to problem behavior is determined by the balance between instigators and controls across the three systems. This study may guide development of interventions with greater potential to prevent the emergence of alcohol abuse and dependence during adolescence and early adulthood. This overview provides a context for understanding the significance of the social ecology in understanding African American adolescents’ behavior in urban public housing. Literature Review
Personality System Variables and Alcohol Use
Personality system variables include sociocognitive variables, such as values, beliefs, and attitudes along with psychological variables like depression (Jessor & Jessor, 1977). Evidence suggests that intention to use alcohol is an important predictor of alcohol use in both youths and adults (Sullivan & Farrell, 1999). Without a doubt, beliefs about social norms for alcohol consumption, weighing the costs and benefits of alcohol use, and prior experiences with alcohol may influence future use. Furthermore, there is a substantial body of research that links various adolescent psychological factors, including attitude toward delinquency, to substance abuse (for example, Luengo, Carrillo-dela-Pena, Otero, & Romero, 1994). Research evidence suggests that youths exhibiting moral disengagement, including favorable attitudes toward problem behaviors, were more likely to engage in alcohol use compared with youths who did not (Welte, Barnes, Hoffman, & Dintcheff, 1999). However, adolescents with favorable attitudes toward social institutions, such as school, were less likely to abuse alcohol than were those with unfavorable attitudes (Guo, Collins, Hill, & Hawkins, 2000). Youth substance use, including alcohol use, is also associated with psychological variables—notably depression (Repetto, Zimmerman, & Caldwell, 2004; Schwinn, Schinke, & Trent, 2010). These findings are suggestive; youths in high-risk impoverished environments may resort to using alcohol as a way to cope with depressive symptoms. However, the question of whether depression leads to or is a consequence of alcohol use remains unresolved.
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Environment System Variables and Alcohol Use
During adolescence, peers greatly influence and form an important behavioral reference for an adolescent (Brown & Klute, 2006). Research has documented similarities in levels of risk behavior among adolescents within the same peer group (see Wright & Fitzpatrick, 2004). In fact, one of the most consistent and strongest predictors of early initiation of alcohol use is exposure to others who use substances. Undoubtedly, a peer environment perceived to be accepting of substance use or misuse may be inviting to an adolescent struggling to cope with the stressors associated with living in urban public housing. Likewise, peer disagreement with and discouragement from drinking alcohol or smoking cigarettes and marijuana may provide an effective shield against initiating such behaviors.This relationship, however, has not been fully explored among African American adolescents in public housing, but it should be further explored. Emotional support from parents has also been associated with less drinking in racially diverse samples of adolescents (Barnes, Reifman, Farrell, & Dintcheff, 2000). Among African American youths, evidence indicates that perceived emotional support from fathers was associated with less self-reported alcohol use (Caldwell, Sellers, Bernat, & Zimmerman, 2004); mother’s emotional support and encouragement was inversely related to a youth’s alcohol use or misuse. At present, the role of parents in the prevention of substance use and other risk behaviors among African American adolescents is unclear. Some studies have reported positive influences; others have found negative influences on youth risk behaviors, suggesting the need for continuing examination of the influences of both paternal and maternal factors on youths’ risk behaviors. Behavior System Variables and Alcohol Use
Convergent findings across studies of delinquent youths indicate significant overlap between delinquent behaviors and substance use and misuse (Thompson, Riggs, Mukilich, & Crowley, 1996). Recent research (see, for example, Vaughn, Freedenthal, Jenson, & Howard, 2007) has shown that substance use severity and serious delinquency go hand in hand, clustering together along a severitybased gradient. Essentially, this literature indicates that youths’ substance use is positively related to
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their criminal histories. Research also points to an association between alcohol use and misuse with problem behavior such as school suspensions, vandalism, and sexual assault. Moreover, research has linked use and misuse of one substance to use and misuse of another; for example, of all alcohol-consuming youths ages 12 to 17, 32% used marijuana (OJJDP, 2008). In contrast, among non-alcohol-drinking youths, only 2% reported marijuana use. Other scholars have found an association between cigarette smoking and increased alcohol consumption (Harrison, Desai, & McKee, 2008). Similar results have been reported by others (seeYoung, Corley, Stallings, Rhee, Crowley, & Hewitt, 2002). The evidence reviewed in the foregoing discussion provides a sound base from which to investigate the effect of multiple domains on alcohol use among African American adolescents living in urban public housing developments. Significance of Context for African American Youths
The foregoing literature suggests that adolescent alcohol use may be a public health concern with important welfare implications. Although there is a sizable body of research concerning adolescent alcohol use, there is a paucity of research examining alcohol use and its correlates among African American youths in public housing developments, microcosm neighborhoods governed by public policy. This population is at greater risk for alcohol and substance use and misuse and its correlates because of their persistent exposure to poverty and other risk factors for alcohol use (Nebbitt & Lombe, 2007; Wright & Fitzpatrick, 2004). African American youths are more likely to reside in urban public housing neighborhoods than are white and Latino youths (HUDUSER, 2008). Scholars argue that living in impoverished, segregated neighborhoods, like urban public housing, can have a simultaneous inhibitive and promotive effect on adolescents development (García Coll et al., 1996). It is conceivable that adaptation to specific aspects of living in urban public housing developments may be associated with alcohol and other drug use among African American adolescents. Purpose of the Study
The purpose of this study was to examine the prevalence of alcohol consumption in African American adolescents living in urban public housing develop-
ment and to test problem behavior theory (PBT) by investigating the relative impacts of the three system variables: personality system variables (that is, positive attitudes toward alcohol use, negative attitudes toward delinquent behaviors, and depressive symptoms), environment system variables (that is, parental support and exposure to delinquent peers), and behavior system variables (that is, delinquent behavior) on alcohol consumption in this sample. Method
Participants and Research Sites
The study was conducted in public housing developments in three cities in the United States. The three cities were selected because their local housing authorities supported the idea of this investigation. The housing developments in each city were selected on the basis of the structure, support, and size of the development. In short, we selected developments with spaces available for data collection, with support staff to help set up data collection rooms, and with a population large enough to recruit a sample that ensured 90% statistic power. Youths were eligible to participate in the study if they were between the ages of 11 and 21; resided in one of the target housing developments; could demonstrate the capacity to give informed consent; and, if under the age of 18, were able to provide both youth assent and parental written informed consent. The study was advertised using flyers and announcements at the housing developments, local social service agencies, and community centers.The department of recreation provided space in community centers located in each housing development where youths were screened and provided consent and the survey was administrated. After identification of an initial group of potential participants, respondent-driven sampling (RDS) was used to recruit the sample for this study (Heckathorn, 1997). RDS is a form of chain-referral sampling that corrects sampling biases typically associated with snowball and chain-referral sampling by producing a sample that is independent of the initial participants from which sampling begins. RDS is an excellent method to use when conducting research in communities that are highly stigmatized, distrust outsiders, and have strong privacy concerns that lead to low participation in research or unreliable answers designed to protect privacy. Youths eligible for the study were provided with parental consent and youth assent forms. A
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youth who obtained consent was administered the Capacity-to-Consent Screen (CCS) (Zayas, Cabassa, & Perez, 2005), which assessed the youth’s mental capacity to give informed assent. A score above 8 indicates a minimum capacity to give informed consent. Only youths who scored 8 or above on the CCS and who provided parental consent and youth assent (18 and younger) and informed consent (18 and older) were administered the survey instrument for this study (characteristics of the study sample are provided in Table 1). For this study, we focused only on respondents who answered questions on key study variables: past-year alcohol use, personality, and environmental and behavior systems questions (N = 403). Data Collection
Study participants met in groups of 10 to 15 to complete the survey. The survey comprised several standardized measures to assess youths’ perception of their housing development and their parents’ behavior.The instrument also assessed youths’ health-risk behaviors and mental health symptoms.The survey took approximately 40 minutes to complete.Youths received $15 and a snack for their participation (see Nebbitt & Lombe, 2007, for a detailed description of data collection procedures). Measures
Dependent Variable: Past-Year Alcohol Use
Past-year alcohol use was assessed using the Centers for Disease Control and Prevention’s (CDC) Youth Risk Behavior Survey (YRBS).Youths were asked, “In the last year how often have you used alcohol?” Responses range from 0 times to 40 or more times (CDC, 2001). Because of the positively skewed distribution, responses were dichotomized into those who used alcohol in the previous year and those who did not (no = 0 and at least one time in the past year = 1).We also measured lifetime alcohol use through this statement: “I have tried alcohol.” Personality System Variables
Attitude toward delinquency was assessed using the National Youth Survey’s (NYS) Attitudes toward Delinquency subscale. Respondents were asked questions such as “How wrong is it for someone your age to steal something worth less than $5?” and “How wrong is it for someone to attack someone with the idea of seriously hurting or killing them?”
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Responses were rated on a four-point scale ranging from very wrong to not wrong at all. The 14-item Attitude Toward Delinquency subscale demonstrated acceptable reliability with the current sample (α = .94). Items are scored so that a higher score represents a greater perceived wrongness (Elliot, 1987). Attitude toward alcohol consumption was assessed using a single item adapted from NYS’s Attitudes toward Delinquency subscale. The item asked youths, “How wrong is it for someone your age to drink alcohol?” Responses were not wrong at all (4), a little wrong, (3) wrong (2), and very wrong (1). A higher score represents a greater favorability to alcohol consumption (Elliot, 1987). Depressive effects, outcomes, and causes were assessed using the Center for Epidemiologic Studies Depression Scale [CES-D] (Radloff, 1977).The scale includes 20 items that survey mood, somatic complaints, interactions with others, and motor functioning. Responses are rated on a four-point Likert-type scale ranging from 0 to 3, with anchor points in terms of days per week—less than one day = 0 to 5 to 7 days = 3. The theoretical range goes from 0 to 60, with higher scores representing greater depressive symptoms. Perreira, Deeb-Sossa, Harris, and Bollen (2005) suggested that researchers disaggregate CES-D into its underlying concepts of depressive effect indicators, depressive cause indicators, and depressive outcome indicators to equally measure the impact of depression on other variables across racial–ethnic groups. They also argued that these aspects of depression may affect youths’ behavior in different ways. To build on and advance this discussion, we tested these three underlying concepts of CES-D using five depressive effects (depressed, sad, happy, blue, and life), three depressive outcomes (appetite, concentration, and start), and 11 depressive causes. Environment System Variables
Parental Behavior. To assess parental monitoring and involvement, youths completed the Parental Attitude Measure (PAM) (Lamborn, Mounts, Steinberg, & Dornbusch, 1991). This 17-item scale assesses two aspects of parenting behaviors: parental supervision and parental encouragement.The five-item Parental Supervision subscale asks youths the following question: “How much do your parents really know who your friends are?” Items are scored on a four-point Likert-type scale ranging from 1 = doesn’t know to 4 = know exactly. The five-item subscale dem-
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M
SD
Sample (N = 403) % M
SD
% M
SD
Younger Youths Older Youths (n = 174) (n = 229)
Table 1: Prevalence Rates of Alcohol Use and Characteristics of Participants
%
Note: df = 1. *p < .05. **p < .01. ***p < .001. (between younger and older youths)
Alcohol use Lifetime alcohol use 53.9 35.3 68.0 12-month alcohol use 26.8 9.8 39.7 Demographic Age 15.1 2.5 12.7 1.0 17.0 1.6 Male 52.1 48.3 55.0 Personality system Positive attitudes toward alcohol use 1.2 1.4 1.0 1.5 1.2 1.3 Negative attitudes toward delinquent behaviors 47.4 10.0 49.2 9.1 46.0 10.4 Depressive symptoms (composite) 16.7 9.3 14.8 8.1 17.1 10.0 Depressive effects 3.6 3.2 3.3 3.1 3.8 3.3 Depressive causes 7.0 6.9 6.0 6.2 7.7 7.2 Depressive outcomes 1.6 2.1 1.4 1.9 1.8 2.2 Environment system Parent support 41.3 10.3 42.4 10.4 40.5 10.2 Exposure to delinquent peers 24.7 9.2 23.1 7.6 26.0 10.1 Behavior system Delinquent behaviors 21.2 8.8 20.5 8.6 21.7 8.8
Variable
0 – 1 0 – 1 11– 20 0 – 1 0 – 4 14 – 56 0 – 51 0 – 15 0 – 36 0 – 9 14 – 56 14 – 53 14 – 63
χ2 = 41.7*** χ2 = 45.3*** t = 32.3*** χ2 = 1.8 t = 0.4 t = 3.3** t = 2.6** t = 1.1 t = 2.2* t = 1.9 t = 3.0** t = 3.2** t = 1.2
Significance Range
onstrated acceptable reliability (α = .76) with the current sample. The 12-item Parental Encouragement subscale assesses parental encouragement and asks youths, “Does your father/mother, stepfather/ stepmother or the man/woman who takes care of you push you to do your best in whatever you do?” Items are scored on a four-point Likert-type scale ranging from 1 = never to 4 = always.The 12-item scale demonstrated acceptable reliability (α = .88). The PAM is scored by summing the items, with higher values indicating higher levels of supervision and encouragement. Exposure to Delinquent Peers. The Exposure to Delinquent Peers scale from the NYS (Elliot, 1987) was used to measure peer involvement in delinquent behavior. This scale asked youths the number of their close friends who have engaged in various types of delinquent behaviors over the past year.The delinquent behaviors assessed by this measure ranged from “alcohol use” to “pressured someone to have sex with them.” The 14-item scale demonstrated acceptable reliability (α = .93). Items are scored so that a higher score indicates greater exposure to delinquent peers (Elliot, 1987).
(Warr & Stafford, 1993). Delinquent acts ranged from “stealing something worth less than $5” to “attack someone with the idea of seriously hurting or killing them.” Response categories ranged from 0 to 12 or more times. The Self-Reported Delinquency scale demonstrated acceptable reliability with the present sample (α = .95).
Behavior System Variables
Results
The Self-Reported Delinquency scale is a 20-item subscale from the NYS (Elliot, 1987). Respondents were asked to report the frequency with which they engaged in a variety of delinquent behaviors in the last year. In the original survey, each item consisted of two parts: raw frequency and rate. In the present study, only raw frequencies were collected, with the highest category scored as 12 or more times. This approach builds on other studies
Prevalence Rates of Alcohol Use and Participant Characteristics
Data Analysis
Chi-square and independent samples t tests were performed to examine whether demographics, personality, environment, and behavior system variables differ by age group: younger youths (11 through 14 years) compared with older youths (15 through 20 years) (see Table 1). Zero-order correlations were used to assess associations among study variables (see Table 2). Finally, multiple logistic regression analyses were performed to estimate the relative contributions of demographics, personality, environment, and behavior system variables in the prediction of past-year alcohol use (see Table 3). Problems with multicollinearity were not observed; all tolerance values exceeded .25 (Fox, 1991). Analyses were conducted using SAS 9.1 (SAS Institute Inc., 2006).
More than half of the respondents were male (52.1%). Ages ranged from 11 years to 20 years, with a mean age of 15.1 and a standard deviation of 2.5 years. Forty-eight percent were younger youths (average age = 12.7 years). At the time of interview, more than half of the participants (53.9%) reported having ever con-
Table 2: Bivariate Correlations (N = 403)
1
2
1. Alcohol use (past year) — 2. Older youths .34 — 3. Male .05 .07 4. Positive attitudes toward alcohol use .08 .06 5. Negative attitudes toward delinquent behaviors –.17 –.16 6. Depressive effects .20 .08 7. Depressive causes .25 .12 8. Depressive outcomes .14 .11 9. Parent support –.06 –.09 10. Exposure to delinquent peers .26 .16 11. Delinquent behaviors .26 .07
3
4
5
6
7
8
9
— .06 — –.11 .02 — –.06 .10 –.07 — –.02 .10 –.16 .79 — –.01 .08 –.11 .72 .78 — –.08 –.06 .28 –.01 –.03 –.03 — .20 .11 –.28 .21 .30 .26 –.19 .23 .07 –.33 .15 .26 .23 –.24
10
11
— .56
—
Note: Correlation coefficients in boldface are significant at p < .05.
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Table 3: Predictors of Alcohol Use among African American Adolescents Living in Urban Public Housing (N = 403) Predictor OR
Model 1
Model 2
(95% CI) OR
(95% CI) OR
Model 3
Demographic variables Older youths 5.5 (3.0, 10.0) 5.5 (3.0, 10.0) 5.5 (3.0, 9.9) Male 0.8 (0.5, 1.4) 0.9 (0.5, 1.5) 0.9 (0.5, 1.5) Personality system variables Positive attitudes toward alcohol use 1.1 (0.9, 1.3) 1.1 (0.9, 1.3) 1.1 (0.9, 1.3) Negative attitudes toward delinquent behaviors 0.9 (0.9, 1.0) 0.9 (0.9, 1.0) 0.9 (0.9, 1.0) Depressive symptoms (composite) 1.0 (0.9, 1.0) Depressive effects 1.1 (1.0, 1.2) Depressive causes 1.1 (1.0, 1.1) Depressive outcomes Environment system variables Parent support 1.0 (0.9, 1.0) 1.0 (0.9, 1.0) 1.0 (0.9, 1.0) Exposure to delinquent peers 1.0 (0.9, 1.0) 1.0 (0.9, 1.1) 1.0 (0.9, 1.1) Behavior system variable Delinquent behaviors 1.0 (0.9, 1.0) 1.0 (0.9, 1.0) 1.0 (0.9, 1.0) –2 log likelihood Likelihood ratio χ2
Model 4
(95% CI) OR
(95% CI)
5.6 0.8
(3.1, 10.1) (0.5, 1.4)
1.1
(0.9, 1.3)
0.9
(0.9, 1.0)
1.0
(0.9, 1.2)
1.0 (0.9, 1.0) 1.03 (1.0, 1.1) 1.03 (1.0, 1.1)
391.5
386.6
385.5
392.9
75.6***
81.9***
83.0***
75.6***
Note: ORs (odds ratios) and 95% CIs (confidence intervals) in boldface are significant at p < .05. ***p < .001.
sumed alcohol; 26.8% had used alcohol in the past 12 months. Younger youths reported significantly lower levels of lifetime alcohol use (35.3% versus 68.0%). Similarly, younger youths had lower rates of alcohol use in the past 12 months compared with older youths (9.8% versus 39.7%). Significant differences were indicated in personality systems variables. Attitude toward delinquent behaviors differed between the two age groups. Younger youths reported significantly higher scores of negative attitudes toward delinquent behavior than did older youths (M = 49.2 versus 46.0). Older youths reported higher scores on the composite measure of depressive symptoms (M = 17.1 versus 14.8) and, particularly, depressive causes (M = 7.7 versus 6.0). Little variation was indicated in positive attitudes toward alcohol use by age, depressive effects, and depressive outcomes. Furthermore, we noted significant variations in environmental system variable between the two age groups. Younger respondents reported higher scores on the measure of parental support (M = 42.4 versus 40.5), and older respondents reported higher scores on the measure of exposure to deviant peers (M = 26.0 versus 23.1). The behavior system variable did not differ between
the two groups. See Table 1 for a detailed description of these results. Bivariate Correlations of Study Variables
A preliminary overview of how study variables may be related with one another is provided in Table 2. As expected, alcohol use in the past year was positively related to age, the three depressive symptom variables, exposure to delinquent peers, and delinquent behaviors. However, alcohol use was inversely related to negative attitudes toward delinquent behaviors. Negative attitudes toward delinquent behaviors were negatively correlated with depressive causes, depressive outcomes, exposure to delinquent peers, and delinquent behaviors and positively related to parental support. Positive attitudes toward alcohol were positively correlated with depressive effects and depressive causes. Depressive effects, depressive causes, and depressive outcomes were highly correlated with each other, with Pearson correlation coefficient values ranging from .72 to .79. Parental support was negatively related to exposure to delinquent peers and delinquent behaviors. Exposure to delinquent peers was positively associated with the three depressive symptom variables and delinquent
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behaviors. Delinquent behaviors were also positively correlated with the three depressive symptoms variables.These correlations were statistically significant at p < .05. Multiple Logistic Regression Analyses for Predicting Alcohol Use
Results of the multiple logistic regression predicting alcohol use are presented in Table 3. The first model [F(8, 394) = 75.6, p < .001] contained the composite score of depressive symptoms and the three PBT system variables. Although significant, the composite score of depressive symptoms did not predict adolescent alcohol use. Age was the only predictor of alcohol use in this model. Model 2 included depressive effects and was significant [F(8, 394) = 81.9, p < .001]. Youths with depressive effects were 10% more likely to consume alcohol than youths without depressive effects. Model 3 added depressive causes. This too was significant [F(8, 394) = 83.0, p < .001] and showed that youths with depressive causes were 10% more likely to drink alcohol than youths without depressive causes. Age remained significant in these models. In contrast, model 4 [F(8, 394) = 75.6, p < .001] revealed that depressive outcomes were not a significant predictor of alcohol use. Exposure to delinquent peers and delinquent behaviors positively predicted alcohol use in this model. We also explored whether age interacted with the three PBT system variables in predicting alcohol use. No significant interaction was found. The nonsignificant deviance and Pearson goodness-of-fit statistics of the four regression models also indicated no interactions (Allison, 1999). Discussion
Overall, this study provides insight into the annual prevalence and correlates of alcohol use among African American adolescents in public housing developments.The sample reported a 53.9% prevalence of ever using alcohol; this compares with a 72.5% prevalence of ever used alcohol for all youths nationally and a 67.7% prevalence for African American youths nationally (YRBS, 2010). The sample reported a 26.8% prevalence of current alcohol use, compared with a 41.8% prevalence nationally and a 33.4% prevalence for African American youths nationally (YRBS, 2010). The prevalence of lifetime use in this sample was also lower than that for the samples in other national
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studies (see, for example, Johnston, O’Malley, Bachman, & Schulenberg, 2006); however, current prevalence in this sample was higher than it was in another national sample (see, for example, SAMHSA, 2007). In addition, compared with older youths, younger youths had lower rates of alcohol use in the past 12 months (9.8% versus 39.7%).These findings reflect national patterns of alcohol use during adolescence. For example, theYRBS (2010) reported that youths in grades 10, 11, and 12 reported higher lifetime and current alcohol use than did youths in ninth grade. Findings are also similar to those of other national studies (Johnston et al., 2006) Our study also lends support to PBT. We found a significant relationship between personality, environment, and behavior system variables and the annual prevalence of alcohol use. In particular, environment system variables (that is, exposure to delinquent peers) and a behavior system variable (that is, delinquent behaviors) were stronger than personality system variables (that is, depressive effects and causes) in predicting adolescent alcohol use in the past year (see models 2, 3, and 4). Specifically, depressive effects and causes, delinquent behavior, and exposure to delinquent peers were positively related to the annual prevalence of alcohol consumption. The relationship between alcohol use and depressive symptoms has been reported by others (Repetto et al., 2004; Schwinn et al., 2010); however, our findings suggest that depressive effects and causes are stronger predictors of alcohol use than depressive outcomes among African American youths in public housing.This finding, though preliminary, advances our knowledge of adolescent alcohol use and depression. Furthermore, this finding seems consistent with the progression of depression. It could be that when youths reach the stage of depression, at which they have lost their motivation, appetite, and concentration, the effects of alcohol consumption may no longer suffice. It is also likely that when urban youths experience depressive outcomes, they may resort to using harder drugs. More research is needed to explore these relationships. Among the environmental variables, only exposure to delinquent peers was related to alcohol use. Indeed, a peer environment that is tolerant of antisocial behavior may be inviting to an adolescent coping with the stress of living in a public housing development. In fact, one of the most consistent and strongest predictors of early initiation and alcohol drinking is exposure to peers who use substances
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(Kosterman, Hawkins, Catalano, & Abbott, 2000). Delinquent behavior was also related to alcohol use in the past year, underscoring the overlap between delinquent behaviors and substance use and misuse. Contrary to annotations made in previous research, parental support was not associated with alcohol use (Barnes et al., 2000) and neither was attitude toward alcohol use (Luengo et al., 1994). Furthermore, there was little evidence to support an association between attitude toward delinquent behaviors and alcohol use in the past year. Study Limitations
A number of limitations are acknowledged. Although recruitment of youths was voluntary, the selection of cities for inclusion in the study was based on the convenience of accessing residents and the willingness of the local housing authorities to work with us.This has potential to heighten the probability of sampling bias.Youths were recruited using respondent-driven sampling. This technique, like other chain referral techniques, could be influenced by participants with large social networks. However, we attempted to control for this effect by limiting the number of referrals made into the study by each participant. Furthermore, accuracy of the data is limited by the accuracy with which respondents recalled and self-reported their situation. Data were collected using group administration, which could bias results due to peer effects on individuals’ responses. Moreover, there may be unobserved covariates such as cultural norms and child abuse and neglect that may be influencing the likelihood of using alcohol in the past year in this sample of urban youths. Given the small effect size and the contextual factors of urban public housing, caution should be exercised in interpreting results of this study. Also, the study’s cross-sectional design does not lend itself to establishing causality. Despite these limitations, we believe that our study yields some novel information and points to implications for practice. Conclusion: Implications for Practice
The results of this study indicate that delinquent behavior was related to alcohol use in the past year. This is noteworthy because it introduces a unique role for community-based asset-building programs, such as Youth Ambassadors and Boys & Girls Clubs. Such programs could work with youths in poor
urban communities in helping them develop positive adaptations, leading to full realization of their potential. Other predictors of alcohol use included depressive causes and effects, which point to a potential feedback-loop effect. For instance, youths in public housing often experience vulnerabilities related to their socioeconomic condition, limited access to health care, and other environmental stressors, thus making them susceptible to alcohol use. However, alcohol use, in itself, may heighten depressive symptoms.This observation is noteworthy and may suggest the need for improved communitybased substance abuse and mental health surveillance systems. Improved surveillance systems could identify youths with depressive symptoms early and provide preventive interventions. Future research, using a longitudinal design, could investigate the path through which depressive symptoms influence alcohol use and misuse. Such findings could provide more clarity and understanding on the feedbackloop effect. Age also was related to alcohol use. Given findings of prior research pointing to an association between young age of drinking onset and higher likelihood of developing alcohol-related disorders (Odgers et al., 2008), a useful starting point could be implementation of interventions with potential to delay the age at first use. Another area of focus could be exploration of the relationship between intention to use alcohol and the annual prevalence of use. This is important in that intention often precedes actual behavior; hence, understanding intention to use or to not use alcohol may have important implications for intervention development. References Allison, P. D. (1999). Logistic regression using SAS:Theory and practice. Cary, NC: SAS Institute. Barnes, G., Reifman, A., Farrell, M., & Dintcheff, B. (2000). The effects of parenting on the development of adolescent alcohol misuse: A six-wave latent growth model. Journal of Marriage and Family, 62, 175–186. Brown, B., & Klute, C. (2006). Friendship, cliques and crowds. In G. R. Adams & M. D. Berzonsky (Eds.), Blackwell handbook of adolescence (pp. 330–348). Malden, MA: Blackwell. Caldwell, C. H., Sellers, R. M., Bernat, D. H., & Zimmerman, M. A. (2004). Racial identity, parental support and alcohol use in a sample of academically at-risk African American high school students. American Journal of Community Psychology, 34(1–2), 71–82. Centers for Disease Control and Prevention. (2001). Conducting your own YRBS. Retrieved from http:// www.cdc.gov/HealthyYouth/yrbs/index.htm Elliot, D. (1987). National Youth Survey [United States]: Wave VII (2nd ICPSR Version) [Computer file].
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Margaret Lombe, PhD, is assistant professor, Graduate School of Social Work, Boston College, Chestnut Hill, MA 02467; e-mail:
[email protected]. Mansoo Yu, PhD, is assistant professor, University of Missouri, Columbia. Von Nebbitt, PhD, is associate professor, University of Illinois at Chicago. Tara Earl, PhD, is manager, ICF International Public Health Division, Atlanta. Original manuscript received October 1, 2010 Final revision received February 7, 2011 Accepted February 14, 2011
Social Work Research Volume 35, Number 3 September 2011
