The results of the comparisons between users and nonusers, shown in , are reinforced by the findings of the single and multiple regression analyses (in Tables and ). Clearly, sixth grade users are significantly different than nonusers on nearly every risk factor that is examined. And, again, large differences between the groups are observed for all of the risk-related behaviors, intrapersonal, and socioenvironmental factors, including violent and delinquent behavior, self-efficacy, outcome expectancies and expectations, and normative estimates. Peer alcohol use and access to alcohol are also especially problematic among those who are using alcohol prior to sixth grade. These comparisons underscore the need to intervene earlier and across a wide spectrum of behavioral, intrapersonal, and socioenvironmental factors. Because of the differences found between users and nonusers on almost every risk factor, it was important to separately examine the predictive factors for each group to determine which specific factors were most influential for each group.
The risk factors that were explored in this study were nearly all significantly associated with sixth grade alcohol use behavior and intentions for both users and nonusers when examining each separate relationship. Thus, at first glimpse, all of these risk factors would be important to consider as the focus for creating intervention objectives for the primary prevention of alcohol use with young teens, regardless of their drinking status as a tween. This is especially important because many of these risk factors, particularly the socioenvironmental and intrapersonal factors, have been amenable to intervention (Perry, 1999
). The relationships between the risk factors and alcohol use behavior and intentions were stronger for users than for nonusers, with higher estimates in the models. This is likely because of greater variability and range in the outcome measure because users exhibited alcohol use intentions and behavior, whereas nonusers (by definition) had only intentions to use alcohol. Higher scores on intentions to use alcohol are strong predictors of subsequent alcohol use, so those nonusers with high intentions scores are at greater risk to begin to drink (Marcoux & Shope, 1997
). Still, the single regression models provide only limited guidance for intervention design because it is important to select the most potent and modifiable risk factors to create powerful yet efficient prevention programs (Perry, 1999
The multiple regression models narrowed the list of most potent risk factors considerably, yet each model still accounted for a substantial percentage of the individual variance in alcohol use behavior and intentions—35% for nonusers and 56% for users—suggesting that these models were robust, notably so for the users. For the baseline nonusers, among the behavioral variables the powerful associations with other drug use, violence, and less attendance at religious activities have been noted elsewhere (Grant & Dawson, 1997
). It is noteworthy that owning or collecting and being willing to wear or use items that have the name of an alcohol beverage on them were significantly associated with alcohol use behavior and intentions. This is similar to what Pierce and colleagues (1998)
have noted with tobacco use and suggests that owning or even being willing to wear one of these items could increase susceptibility to future alcohol use among nonusers. It appears that efforts to restrict alcohol beverage promotional items may be needed, in the same way that the Master Settlement Agreement banned such items for cigarette manufacturers because they were seen as being too youth oriented (Capehart, 2001
). Among the intrapersonal factors, nonusers with higher alcohol use behavior and intentions scores were more likely to have low self-efficacy and more positive alcohol outcome expectations and expectancies. Thus, not having the confidence to be able to refuse alcohol, not perceiving negative consequences of alcohol use, and not valuing the potential consequences of alcohol use all appear to be critical. These are important to note because these risk factors are amenable to intervention, as was found in Project Northland, in which self-efficacy and outcome expectancies (i.e., functional meanings) were both modified as a result of the intervention (Perry et al., 1996
). Finally, for nonusers, among the socioenvironmental factors, reporting having had offers to drink, having less frequent parent-child communication, and perceiving that more young people drink are all potent risk factors for alcohol use behavior and intentions, suggesting greater peer influence to use and less positive parental influence. Appropriate and amenable targets for intervention might then be to correct normative estimates of drinking if they are too high (Agostinelli & Grube, 2005
), to structure opportunities for greater parent-child communication (Kosterman, Hawkins, Haggerty, Spoth, & Redmond, 2001
), to increase monitoring of those who might offer alcohol to youth, and to provide skills concerning alcohol refusal (Perry et al., 1996
). To a large extent, many of these risk factors have formed the basis for successful prevention programs with young teens (Botvin, Baker, Dusenbury, Botvin, & Diaz, 1995
), but these analyses among the nonusers point to those factors that can be used to strengthen the focus of our programs because they highlight the particular factors associated with the greatest risk for subsequent early onset of alcohol use.
Among baseline users, other related behaviors, including tobacco use, marijuana use, violence, and delinquency, predominate in the multiple regression model. This strongly points to the early covariation of these behaviors and the need to intervene with these youth well before sixth grade. There is ample evidence that children who have conduct disorders or engage in deviant behaviors during early and middle childhood are more likely to continue those behaviors into adolescence, including alcohol use (Caspi, Moffitt, Newman, & Silva, 1996
; Fergusson et al., 2005
; Hawkins et al., 1997
). Thus, early intervention, either through selected or universal programs, is needed to alter the developmental trajectories of these related behaviors during childhood, particularly before these trajectories become less mutable to change in adolescence. Among baseline users, the most potent intrapersonal and socioenvironmental factors included low self-efficacy and more positive expectations of the consequences of drinking, perceptions that more peers drink alcohol, and perceptions that those who drink experience more positive outcomes. These are consistent with others’ research that suggests that children as young as fourth grade who have positive expectancies around alcohol use are more likely to be early users (Hipwell et al., 2005
) and that associating with deviant peers and having poor self-regulatory skills during the tween-age years increase the risk of early alcohol use (National Institute on Alcohol Abuse and Alcoholism, Public Health Service, & National Institutes of Health, 2005
). Thus, it seems critical to intervene earlier than sixth grade for the primary prevention of alcohol use, particularly for higher risk students. However, in PNC, 17% of the students were already users at the beginning of sixth grade, about 1 in 6 youth. In PN, 37% of the sample, about 3 in 8 youth, had “ever” used alcohol and were less responsive to the intervention (Perry et al., 1996
). Thus, the “users” are a substantial proportion of the sixth grade population. It may be important, then, to design a primary prevention program for tweens, prior to sixth grade, that focuses on specific alcohol-related risk factors, such as alcohol use expectancies, expectations, normative estimates, and normative expectations, found to be important in these analyses but that also focuses on the important developmental tasks of that age group—academic achievement, appropriate conduct, and prosocial peer relationships. This approach, intervening at earlier ages, would allow for universal messages about alcohol use and yet also provide support for high-risk students who may be developmentally lagging.
There were some unusual findings that should be noted. Among users, feeling depressed was inversely related to alcohol use. This has not been noted before, and others have found a positive relationship between depression and alcohol use (Donovan et al., 2004
) or no relationship (Greenblatt, 2000
). Thus, this finding is likely because of the question itself, which was a single item asking the students if they felt depressed or sad in the past month and not specifically addressing depressive symptoms. A second unexpected finding was that alcohol users were more likely than nonusers to spend time outside of school being physically active. This included time spent in team sports, with the drill team, biking, walking, or skateboarding. Thus, it may be that the time is not spent under adult supervision but is spent with peers (e.g., skateboarding) who could encourage alcohol use or other related activities in which users may frequently engage. Because peer alcohol use is also much higher among users, it may be that users associate with other users and the potential for access to alcohol increases. Finally, the lack of significant demographic factors in the multiple regression models suggests that with this urban, inner-city population, these factors are less important than the behavioral, intrapersonal, and socioenvironmental factors and that alcohol use is problematic across genders, races/ethnicities, and socioeconomic levels.
This study of users and nonusers of alcohol was strengthened by its large sample size and multiethnic population and by the recentness of the data. The study is limited by being cross-sectional, so that causal inferences cannot be made in the associations presented. Clearly, there is need to follow-up on these analyses as the students mature. Another limitation of this study is the dependent variable: alcohol behaviors and intentions. For sixth grade users of alcohol, the dependent measure included both alcohol behaviors and intentions, whereas for nonusers the dependent measure included only alcohol intentions because, by definition, they did not participate in any current or past alcohol use behaviors. Although there is a difference in the range of the dependent variable for each group, it is one way to explore associations among the behavioral, intrapersonal, and socioenvironmental factors and overall alcohol behaviors and intentions within each of these groups. This is especially true because the alpha coefficient for the entire scale is .82. As noted, intentions to drink are strongly related to future behaviors.
Future research is needed to explore the cross-sectional associations among the behavioral, intrapersonal, and socioenvironmental factors with alcohol intentions alone. In addition, longitudinal research that explores the associations of earlier behavioral, intrapersonal, and socioenvironmental factors with later behaviors is needed. This longitudinal research will allow for the creation of the groups of users and nonusers at earlier ages and for the exploration of how the behavioral, intrapersonal, and socioenvironm ental factors influence alcohol behavior in each group.
In summary, it appears that the focus of Project Northland and PNC was appropriate for nonusers. The primary factors addressed in these interventions are the behavioral, intrapersonal, and socioenvironmental factors presented. Thus, it will be of interest to note which of the factors mediate subsequent alcohol use and the outcomes of the PNC intervention. Early users of alcohol, however, are already at very high risk in all domains, and thus earlier intervention is critical to alter these risk factors prior to sixth grade, while students are in their tweens, by particularly focusing on the risk factors that most strongly predicted use and were differentially distributed across users and nonusers. Although some research has been done in the primary prevention of developmental problems with tweens (Hawkins, Catalano, Kosterman, Abbott, & Hill, 1999
), these data suggest that both a developmental focus and a specific focus on particular alcohol-related risk factors (e.g., outcome expectancies or normative estimates) are needed to affect those at highest risk for teen alcohol use.