Predicting high-riskversushigher-risk substance use during late adolescence from early adolescent risk factors using latent class analysis

Abstract

Much of the existing risk factor literature focuses on identifying predictors of low-levels of substance use versus higher-levels of substance use. In this paper, we explore more nuanced patterns of alcohol, tobacco, and other drug (ATOD) use during late adolescence. Our aims were to: 1) identify subgroups of youth with qualitatively different patterns of ATOD use; and 2) explore whether membership among qualitatively distinct, high-risk classes could be predicted based on early adolescent risk factors. Data came from a selected subsample of the National Longitudinal Survey of Youth (n = 1,689). Predictors were measured when youth were about 12 years old; ATOD use was assessed when youth were aged 17 years. Results showed that adolescent ATOD use is not a homogenous behavior. Four distinct classes of adolescent ATOD users were derived. Each class had a qualitatively distinct and discriminable pattern of ATOD use. Ecological predictors were shown to differentiate between latent classes, with peer factors playing a particularly important role in differentiating between high-risk and higher-risk users. Implications for prevention and limitations are discussed.

Dynamic Patterns of Adolescent Substance Use: Results From a Nationally Representative Sample of High School Students

“…In previous analyses of the baseline of the longitudinal data analyzed in the current study, Conway and colleagues (2013) used a nationally representative sample of 10th-grade students and found four patterns ("classes") of users: majority nonusers, alcohol users, marijuana users, and polysubstance users (e.g., alcohol, tobacco, marijuana, and other illicit drug use). Four other studies using national and local samples found four or five classes of substance users, with a consistent finding of a polysubstance-user class using alcohol, tobacco, marijuana, and/or other drugs (Connell et al, 2010;Cranford et al, 2013;Dierker et al, 2007;Lamont et al, 2014;Whitesell et al, 2006). These studies were limited to cross-sectional analysis.…”

“…Although prior findings are not always consistent, and bidirectional in nature, studies have shown that teens who have depressive symptoms are at higher risk for substance use, dependence, and multiplesubstance use (Conway et al, 2013;Costello et al, 1999;Fleming et al, 2008;Hussong et al, 1998;King et al, 2004;Loeber et al, 1998;Marmorstein et al, 2010;Sihvola et al, 2008;Stice et al 1998). Associating with peers who use substances has also been related to initiation, greater use (D' Amico & McCarthy, 2006), and multiple-substance use (Lamont et al, 2014). Finally, positive parenting practices (e.g., responsiveness, monitoring, setting expectations for nonuse) and family relationships have all been associated with delayed or reduced substance use (Avenevoli et al, 2005;Ryan et al, 2010).…”

The Effect of Residence, School Status, Work Status, and Social Influence on the Prevalence of Alcohol Use Among Emerging Adults

“…Peer drinking has consistently been related to adolescent alcohol use (D'Amico & McCarthy, 2006) and may work through social norms or direct social pressure to drink (Borsari & Carey, 2001;Brooks-Russell et al, 2014). Peer infl uence would be expected to be particularly strong during adolescence and emerging adulthood (Lamont et al, 2014; White & Jackson, 2004/2005. Because social infl uence is associated with drinking prevalence and may vary according to environmental circumstances, with peer infl uences potentially more intense in group living situations, it is of particular interest in this study in terms of its association with drinking and as a potential confounder of the association between drinking and environmental context.…”

Substance Use Profiles of Urban American Indian Adolescents: A Latent Class Analysis

“…In comparison, LCA is a person-centered analysis that empirically identifies homogenous subgroups of a larger population, distinguishes intragroup differences, and assumes heterogeneity in the data (Nurius & Macy, 2008; Reinke, Herman, Petras, & Ialongo, 2008). LCA can capture and summarize information on the use of multiple substances without posing a priori categories (Lamont, Woodlief, & Malone, 2014). …”

“…Numerous studies employing LCA with nationally representative samples have examined adolescent substance use (Bohnert et al, 2014; Marti, Stice, & Springer, 2010; Riehman, Stephens, & Schurig, 2009; Shin, Hong, & Hazen, 2010; Lamont et al, 2014), but findings have varied in the number and type of classes of adolescent substance users. The variability may be due to differences in the age of the sample, period of substance use examined, and number of substances in the analysis (Conway et al, 2013).…”