Substance Use Among Adolescents in California: A

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Aug 23, 2013 - Tamika D. Gilreath1, Ron A. Astor1, Joey N. Estrada2, Renee M. Johnson3 ... 1School of Social Work, University of Southern California, Los ... with less than 3 days of recent use; 9.2%), and frequent ... To move the field forward, we examine patterns of sub- .... measurement error related to class assignment.

Substance Use & Misuse, Early Online:1–8, 2013 C 2013 Informa Healthcare USA, Inc. Copyright  ISSN: 1082-6084 print / 1532-2491 online DOI: 10.3109/10826084.2013.824468


Substance Use Among Adolescents in California: A Latent Class Analysis Tamika D. Gilreath1 , Ron A. Astor1 , Joey N. Estrada2 , Renee M. Johnson3 , Rami Benbenishty4 and Jennifer Beth Unger5

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School of Social Work, University of Southern California, Los Angeles, California, USA; 2 Department of Counseling and School Psychology, San Diego State University, San Diego, California, USA; 3 Department of Community Health Sciences, Boston University, Boston, Massachusetts, USA; 4 School of Social Work, Bar Ilan University, Ramat Gan, Israel; 5 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA To move the field forward, we examine patterns of substance use among youth in the state of California. National data indicate that substantial proportions of youth are using alcohol, tobacco, and marijuana. National Youth Risk Behavior Surveillance Survey (YRBS) data from 2009 show that 73% of all high school-attending youth reported lifetime use of alcohol, whereas 42% reported past 30-day use (Centers for Disease Control and Prevention, 2011). The lifetime and past 30-day prevalence of cigarette use was lower than for alcohol, but still substantial (respectively, 46% and 20%). More than one-third of adolescents reported lifetime marijuana use, and 21% reported past 30-day use (Centers for Disease Control and Prevention, 2011). Although early adolescents are less likely than high school-attending youth to use alcohol, tobacco, and marijuana (Johnston, O’Malley, Bachman, & Schulenberg, 2012), the adverse consequences of use for this age group are substantial with earlier initiation having serious developmental and social implications (Newcomb, Scheier, & Bentler, 1997; Squeglia, Jacobus, & Tapert, 2009). Although informative, binary measures of lifetime and past 30-day use of alcohol, tobacco, and marijuana provide only part of the picture of patterns of adolescent substance use. Comprehensive information about how frequently youth use substances, as well as about joint use of substances (i.e., “polysubstance use”) is also needed. Although substance use researchers emphasize that it is rare for youth to use only one substance, it is uncommon to see nuanced analyses of polysubstance use, and such analyses rarely account for recency and frequency of use (e.g., number of times used). Importantly, the number and types of substances used by the same person have been shown to predict other behavioral health risks (Connell, Gilreath,

Data from the California Healthy Kids Survey of 7th, 9th, and 11th graders were used to identify latent classes/clusters of alcohol, tobacco, and marijuana use (N = 418,702). Analyses revealed four latent classes of substance use, which included nonusers (61.1%), alcohol experimenters (some recent alcohol use; 22.8%), mild polysubstance users (lifetime use of all substances with less than 3 days of recent use; 9.2%), and frequent polysubstance users (used all substances three or more times in the past month; 6.9%). The results revealed that alcohol and marijuana use are salient to California adolescents. This information can be used to target and tailor school-based prevention efforts. Keywords

adolescents, alcohol, tobacco, marijuana


Adolescence is a crucial period for the initiation of substance use, and use can negatively impact health and well-being in adulthood (Eaton et al., 2010; Grunbaum et al., 2004; Irwin, Burg, & Cart, 2002; Jessor, 1991). The health and financial costs associated with substance abuse are excessive, making prevention an important goal for policymakers and practitioners (Ettner et al., 2006). For instance, tobacco use is responsible for the majority of preventable diseases and death in the United States (USDHHS, 2004). It has been estimated that underage drinking costs $62 billion annually. This figure includes medical and societal expenses, costs associated with diminished quality of life related to motor vehicle crashes, and other consequences of drinking behavior (Miller, Levy, Spicer, & Taylor, 2006). These realities underscore the importance of prevention research on adolescent substance use.

Address correspondence to Tamika D. Gilreath, School of Social Work, University of Southern California, 669 W 34th Street, Los Angeles, CA 90089-0411, USA; E-mail: [email protected]


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& Hansen, 2009; Gilreath, Connell, & Leventhal, 2012). Connell, Gilreath, and Hansen (2009) found that—along with frequency and recency of substance use—the use of multiple substances was strongly associated with sexual risk behavior (Connell, Gilreath, & Hansen, 2009). Therefore, more detailed data about patterns of adolescent substance use could be used to inform and enhance prevention programs to address substance use as well as other risk behaviors. Latent class analysis (LCA) is one of the best methodological tools available to understand nuanced patterns of risk and risk behaviors (Lanza, Rhoades, Greenberg, & Cox, 2011; Sullivan, Childs, & O’Connell, 2010). There is significant variation in patterns of adolescent substance use by geographic location (Connell, et al., 2009; Connell, Gilreath, Aklin, & Brex, 2010). As an illustration, several key differences are apparent when comparing two articles that include similar LCAs of adolescent substance use. The first article used a nationally representative sample and the second included nonmetropolitan youth in New England (Connell et al., 2009, 2010). Among those classified as “frequent polysubstance users (PSUs)”, the response probabilities (i.e., the likelihood of having used any one drug or a combination of drugs) were distinct for the two populations. Nationally, frequent PSUs had a 52% chance of drinking alcohol on more than 6 days in the past month, compared to a 66% chance among youth in the New England study. Additionally, in the national study frequent PSUs had a 47% chance of having used marijuana on >6 days in the past month, compared to a 75% chance in the New England sample. Patterns of substance use among California youth are likely to differ from national estimates for several reasons. First, California is the most populous and demographically diverse state in the United States, and 25% of its population is younger than 18 years (U.S. Census Bureau, 2012). Additionally, California was the first state to enact a comprehensive statewide tobacco control policy in 1989 with the overarching goal of reducing tobacco use among its population (Bal, Kizer, Felton, Mozar, & Niemeyer, 1990). Finally, California passed the first medical marijuana proposition in 1996, and established dispensaries in 2003 (California Department of Public Health, 2012). Medical marijuana laws in California are less stringent compared to other states. Recent data indicate that the national prevalence of adolescent marijuana use has increased since 2008; it has been suggested that the increase is due to in youths’ declining perceptions of marijuana as dangerous (Johnston et al., 2012; Kuehn, 2011). Because California youth have grown up in an era of medical marijuana and comprehensive tobacco control, their patterns of substance use may differ from youth in the rest of the country. The current study explicates patterns of substance use that take account for both frequency and recency of use of alcohol, tobacco, and marijuana using LCA. We also examine how demographic factors are associated with substance use. Few studies have employed LCAs on largescale or representative databases of youth; (Cleveland, Collins, Lanza, Greenberg, & Feinberg, 2010; Connell

et al., 2009, 2010; Gilreath et al., 2012) and none have focused on California youth specifically. What is learned will be used to improve our understanding of the etiology of substance use and to inform prevention strategies in California and beyond. METHODS

The data used in this study are from the California Healthy Kids Survey (CHKS), conducted by WestEd, a nonprofit research, development, and service agency in collaboration with the California Department of Education (CDE). The CHKS is a biennial survey that consists of a core survey module that gathers demographic background data (e.g., grade, sex, and race/ethnicity) and inquires about students’ health-related behaviors (e.g., tobacco use, alcohol use, drug use, violence behaviors), and school safety. The CHKS was required to be administered biennially by all schools that received Title IV funding under the federal Safe and Drug Free Schools and Communities Act or the State’s Tobacco Use Prevention Education program (∼85% of districts statewide). Under such mandates, schools must survey a representative district-wide grade-level sample of students in the 5th, 7th, 9th, and 11th grades according to standards set forth by the CDE. The CDE sampling procedure requires that (1) 100% of all district schools participate; or 100% of all selected schools from a district-level approved sampling plan participate; (2) an appropriate class subject or class period was identified and used to collect data; (3) 100% of selected classrooms participated; AND (4) The number of completed, usable answer forms obtained per grade was 60% or more of the selected sample; OR (5) If active parental consent is used, 70% or more parents within each grade’s selected sample returned signed permission forms, either consenting or not consenting to their child’s participation. Additional details of the recommended sampling procedure is described in detail elsewhere (Austin & Duerr, 2004). Prior to the survey being administered at a school site, parental consent was gathered by each school district through the CDE and WestEd for each participant. The core survey was administered by school staff members familiar with questionnaire administration or by WestEd employees if a school site chose to hire professionally trained survey administrators. Proctoring instructions were given to all survey administrators and an introductory script was read to the student participants. Participants were encouraged to answer questions honestly and assured their responses would remain anonymous. Participants were allowed to withdraw from the survey at any time. The survey took approximately 50 min to complete. CHKS data collected for the 2005–2007 academic school years from students who self-reported as Hispanic ethnicity, African American, or White will be used in the present study. A weighting procedure was used to adjust the total of grade level respondents to represent the total district enrollment for the particular grade levels of interest, and district level clustering was also accounted for.



TABLE 1. Overall demographic and substance use characteristics, CHKS, 2005–2007 Demographic characteristics Sex

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Male Female Grade 7th grade 9th grade 11th grade Race/Ethnicity Hispanic White African American

Risk behaviors

Weighted %

Unweighted n

47.5 52.5

201,913 219,862

36.2 35.1 28.7

152,023 148,929 123,934

58.8 33.1 8.1

223,072 172,242 29,572

Alcohol use Never used No recent use 1 or 2 days recent use 3 to 9 days recent use 10 to 30 days recent use Tobacco use Never used No recent use 1 or 2 days recent use 3 to 9 days recent use 10 to 30 days recent use Marijuana use Never used No recent use 1 or 2 days recent use 3 to 9 days recent use 10 to 30 days recent use

District-level consent procedures were followed and the present study has appropriate IRB approval from the University of Southern California. Alcohol, tobacco, and marijuana use were each assessed using a single item that gauged lifetime use and frequency of past 30-day use. Specifically, the response categories were: never used, lifetime use without any past month use, used 1–2 days in past month, used 3–9 days in past month, or used 10 or more days in the past month. LCA was conducted using Mplus 6.1 (Lubke & Muth´en, 2005; McCutcheon, 1987). LCA is used to identify homogeneous subgroups within a heterogeneous population (Auerbach & Collins, 2006; Lanza, Collins, Lemmon, & Schafer, 2007; Magidson & Vermunt, 2002). Multinomial logistic regression analyses were completed simultaneously with class estimation to account for measurement error related to class assignment. Gender, race/ethnicity (White, African American, and Hispanic/a), and educational level (7th, 9th, 11th) were included as demographic covariates in that regression in that regression. A series of models was run to determine the appropriate number of classes for substance use starting with

Weighted %

Unweighted n

55.8 20.0 13.7 6.8 3.6

234,199 80,112 55,813 29,362 15,475

71.3 20.4 4.2 1.9 2.2

299,988 81,322 17,330 8,343 10,304

77.6 11.9 4.4 2.8 3.4

325,374 47,336 17,822 11,230 14,809

a one-class (no covariates) model followed by a series of models with covariates specifying increased number of classes (e.g., two-class, three-class) representing different patterns of substance use behavior. Optimal model selection was based upon recommended indices including low Adjusted Bayesian Information Criterion (BIC) relative to other models, significant Lo-Mendell-Rubin Likelihood Ratio Test (LMR LRT), and acceptable quality of classification (Nylund, Asparouhov, & Muth´en, 2007). RESULTS

The sample was 47.5% male. Thirty-six percent of the students were in 7th; 34.1% were in 9th grade. Hispanics comprised 58.8% of the sample, followed by Whites (33.1%), and African Americans (8.1%). Prevalence of lifetime use was 44.1% for alcohol, 28.7% for tobacco, and 22.5% for marijuana (Table 1). As shown in Table 2, a four-class model provided the best overall fit to the data for substance use behavior. This is exemplified by the nonsignificant p-value for

TABLE 2. Fit statistic comparisons of latent class analysis models of substance use in California Substance use in California Model 1 2 3 4 5

Description One-class (no covariates) Two-class Three-class Four-class Five-class

Adjusted BIC

LMR LRT p-value


5,323,905.862 2055183.168 2009106.900 2002007.651 1,992,189.895

.0000 .0008 .0000 .000 .7602

– .80 .756 .738 .712

Note: BIC = Bayesian Information Criterion. LMR LRT = Lo-Mendell-Rubin Likelihood Ratio Test p-value for (K-1)-classes. A significant p-value indicates that the (K-1)-class model should be rejected in favor of a model with at least K-classes. Best fitting models identified in bold.



TABLE 3. Conditional probabilities of substance use (n = 418, 702)

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Class prevalence Tobacco Never used No recent use 1 or 2 days recent use 3 to 9 days recent use 10 to 30 days recent use Alcohol Never used No recent use 1 or 2 days recent use 3 to 9 days recent use 10 to 30 days recent use Marijuana Never used No recent use 1 or 2 days recent use 3 to 9 days recent use 10 to 30 days recent use

Frequent polysubstance users 6.9%

Moderate polysubstance users 9.2%

Polysubstance experimenters 22.8%

Nonusers 61.1%

.107 .247 .164 .179 .302

.233 .448 .260 .057 .002

.458 .518 .017 .003 .004

.948 .047 .004 .001 .001

.028 .084 .170 .364 .355

.024 .115 .520 .308 .034

.165 .554 .213 .043 .025

.844 .095 .048 .008 .005

.080 .184 .139 .180 .417

.310 .289 .258 .121 .022

.591 .341 .039 .016 .014

.992 .003 .003 .001 .001

the five-class model, which indicates that the (K-1)-class model should not be rejected in favor of a model with at least K classes. The four classes were termed: frequent PSUs, moderate PSUs, polysubstance experimenters, and nonusers (n = 418,702). Sixty-one percent of the respondents were in the nonuser group. These youth reported little or no history of substance use. Conditional probabilities for substance use are summarized in Table 3. Polysubstance experimenters accounted for 22.8% of the sample. The members of this class reported lifetime use, but low likelihood of recent use of alcohol, tobacco, or marijuana. Moderate PSUs accounted for 9.2% of the sample; they had at least a 30% chance of reporting use of alcohol, tobacco, and marijuana on at least 1 day in the past month. The frequent PSUs comprised 6.9% of the sample and had at least a 40% chance of indicating that they used alcohol, tobacco, and marijuana on three or more days in the past month. They had over a 40% chance of reporting using marijuana 10 or more days in the past month. Multinomial logistic regression analyses presented in Table 4 showed that demographic factors influenced class

membership. Not surprisingly, being in a higher grade was associated with membership in any substance use class (i.e., frequent PSU, moderate PSU, or polysubstance experimenter) compared to the nonuser class. Compared to males, females were significantly more likely to be moderate PSUs (OR = 1.31, 95% CI = 1.12–1.54), but were significantly less likely to be frequent PSUs (OR = 0.60, CI = 0.56–0.63). Compared to Whites, African Americans were twice as likely to be polysubstance experimenters (OR = 2.13, CI = 1.29–3.53), and were significantly less likely to be either a moderate or frequent PSU. Hispanic/as were more likely than Whites to be polysubstance experimenters (OR = 2.30, CI = 1.94–2.73) and moderate PSUs (OR = 1.60, CI = 1.33–1.93). DISCUSSION

This study sought to characterize patterns of frequency and recency of alcohol, tobacco, and marijuana use among a large sample of adolescents in California. The results of the present study indicate that there is considerable variation in substance use among youth and that this use is

TABLE 4. Multinomial logistic regression results of substance use in California (n = 418, 702)

Covariates Grade Female African American Hispanic

Polysubstance experimenters vs. nonusers OR (95% CI)

Moderate polysubstance users vs. nonusers OR (95% CI)

Frequent polysubstance users vs. nonusers OR (95%)

1.77 (1.72–1.82) 0.90 (0.80–1.01) 2.13 (1.29–3.53) 2.30 (1.94–2.73)

1.77 (1.66–1.88) 1.31 (1.12–1.54) 0.55 (0.42–0.72) 1.60 (1.33–1.93)

1.87 (1.80–1.94) 0.60 (0.56–0.63) 0.71 (0.62–0.82) 0.94 (0.85–1.03)

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qualitatively and quantitatively different from national findings. Quantitatively, the majority of youth were classified as nonusers (∼61%) compared to only 27.7% nationally (Connell et al., 2009). A majority of the present sample consisted of Hispanic youth (58.8%). Thus, the overall higher prevalence of nonusers found may be attributable to the large proportion of youth who are likely members of immigrant families in California and may not be fully acculturated. Research has shown that adolescents who are less acculturated and recent immigrants are less likely to use drugs in adolescence than those who report greater acculturation (Almeida, Johnson, Godette, & Atsusi, 2012; De la Rosa, 2002). Qualitatively, the response probabilities that defined classes were also divergent. In each of the substance use classes (excluding nonusers) the likelihood of recent tobacco use was lower in the present study compared to national estimates. This was likely driven by the fact that, overall, tobacco use was lower in this population as compared to the nation and underscores the importance of geographic differences in substance use. The rates of tobacco use were also consistently lower than rates of marijuana use making alcohol and marijuana the primary drugs of use across the alcohol experimenters, moderate PSU, and frequent PSU classes. This is different from other largescale findings of adolescent substance use, which generally show alcohol and tobacco use being predominant (Cleveland et al., 2010; Connell et al., 2009). Studies of adult tobacco use and mortality indicate that tobacco use has decreased in California at higher rates than national estimates (Cowling & Yang, 2010; Siegel et al., 2000). This study lends support to similar evidence for adolescents. However, in general rates of marijuana use were comparable to national estimates. Approximately 7% of the youth in California are likely to report that they are using multiple substances frequently in the month prior to completing the survey (frequent PSUs). Given that, substance use in adolescence predisposes youth to numerous negative health and social outcomes (Newcomb et al., 1997; Squeglia et al., 2009). This class of frequent PSUs is of great concern because early initiation increases the likelihood of addiction and substance abuse in adulthood. Finally, African American, Hispanic/a, and White secondary students in California schools vary in likelihood of the conjoint usage of these three substances combined with the frequency and recency of usage. For example, African American youth were significantly less likely to be moderate or frequent PSUs compared to their White counterparts. Surprisingly, Hispanics were as likely as Whites to be classified as frequent PSUs. This is contrary to the assertion that Hispanic youth may have lower rates due to potentially lower levels of acculturation. For example, differences in substance use between African American and White samples show support for religiosity and cultural proscriptions against substance use as protective factors among African American adolescents (Clark, Scarisbrick-Hauser, Gautam, & Wirk, 1999; Wallace, Brown, Bachman, & Laveist, 2003).


The present study does have limitations. First the data are cross-sectional and no exploration of patterns of use over time can be assessed. As with most research on substance use, the data are based on self-report. Third, this study only included students who were present at school and students who were absent or truant could potentially have different patterns of substance use. Finally, the instrument is limited in questions asked about frequency and recency of other drugs (including illicit and prescription medications). National data show that consideration of other drug use including prescription and OTC drugs is important in understanding adolescent substance use and identifying targets for intervention (Connell, et al., 2009; Eaton et al., 2010). Currently, substance use tends to be assessed from a one-dimensional perspective, limited to whether or not respondents report any lifetime use or any recent use of each of the substances, most commonly use of alcohol, tobacco and marijuana (Eaton et al., 2010; Johnston, O’Malley, Bachman, & Schulenberg, 2006). This is understandable, as it can be computationally difficult and requires substantial sample sizes to account for the use of multiple substances at varying frequencies. However, detailed multidimensional understanding of substance use behaviors may be critical in: (1) triaging treatment protocols and interventions when resources are limited to target those who are engaged in the highest levels of polysubstance use; (2) identifying particular combinations of substances used for tailoring treatment; (3) understanding levels of addiction based upon frequency and recency of use. These results suggest that, generally, primary intervention programs in California schools should target alcohol and marijuana use concurrently. More specifically, there is a subgroup of PSU youth who should be targeted for secondary interventions of tobacco, alcohol, and marijuana use. Important differences between frequent PSUs and moderate PSUs show that the former has a 30% chance of using tobacco, alcohol, and/or marijuana 10+ times in the past month. This level of usage places these youth well on their way to substance use disorders, which will have substantial impacts on transitions into adulthood and beyond. Substance use and abuse in adolescence has long been a target of prevention and intervention science. This article provides empirical evidence of the need to better understand the complexity of substance use behaviors and the need for different intervention strategies based upon severity of usage. It is important that public health and prevention scientists be responsive to the growing diversity of needs of the populations they serve and identify ways to maximize efficiency in intervention design and implementation by utilizing not only evidence-based but also data-driven person-centered models such as the one presented.

Declaration of Interest

The authors report no conflicts of interest. The content is the sole responsibility of the authors and does not



necessarily represent the official views of the CDC, the NIH, or the City of Boston. Renee M. Johnson’s work on this publication was supported by grants from the National Institute on Drug Abuse (R03-DA025823; K01-DA31738).

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THE AUTHORS Tamika D. Gilreath, Ph.D., is an Assistant Professor in the School of Social Work at the University of Southern California. Dr. Gilreath’s work focuses on health disparities, substance use, and mental health of vulnerable school-based adolescent populations in the United States, as well as international tobacco consumption among adolescents in sub-Saharan Africa. Overall, her work contributes to the understanding of differential behavioral health outcomes by race, ethnicity, and other social identities using latent variable modeling to explore patterns of co-occurrence of behavioral health indicators.

Ron Astor, Ph.D., is the Thor endowed professor of urban social development at the University of Southern California in the schools of Social Work and the Rossier School of Education. His work examines the role of the physical, social-organizational, and cultural contexts in schools related to different kinds of school violence. He also explores how schools can support students from military families. Findings from his studies have been published in more than 150 scholarly manuscripts and five books published by Oxford University Press and Columbia University, Teachers College Press.

Joey N. Estrada, Jr., Ph.D., received his Ph.D. from the University of Southern California. He received his M.S.W. from UC Los Angeles and his Bachelor’s from UC Santa Barbara. Dr. Estrada’s research interests include school violence, street gang culture, school-based intervention, resiliency, and youth empowerment. His work has been published in major academic journals, and he has presented his research at various national and international research conferences. He is currently conducting research on the risk and protective factors for ganginvolved youth within school communities.

Renee M. Johnson, Ph.D., completed her doctoral studies at the University of North Carolina Gillings School of Global Public Health in 2004 (Chapel Hill, NC). She is currently an Assistant Professor at the Boston University School of Public Health (Boston, MA). Additionally, she is a core faculty member with the Harvard Youth Violence Prevention Center, the Boston Medical Center Injury Prevention Center, and the Social Adjustment and Bullying Prevention Laboratory at BU School of Education ( Prior to joining the Boston University faculty in 2009, she was with the Alonzo Smythe Yerby Postdoctoral Fellowship program at Harvard School of Public Health. Dr. Johnson studies the prevention and epidemiology of suicide, firearm injury, youth violence, and substance use, among adolescents and emerging adults. Her current research examines how neighborhood context impacts initiation of substance use, with a particular emphasis on marijuana use among low-income, urban youth. She is a member of the American Public Health Association and the Society for Prevention Research. Rami Benbenishty, Ph.D., is working with the Israeli Ministry of Education to conduct secondary analyses of their databases and examine the relationships between school climate, victimization, and academic achievements. His work addresses multiple issues in child welfare, with a special emphasis on victimization and maltreatment of children in the family and in schools. With Dr. Ron Astor, he is developing a conceptual and methodological framework addressing the schools nested in their ecological context. He is Co-PI in a project led by Dr. Astor, funded by the Department of Defense Education Activity (DoDEA). He is in charge of ongoing formative and summative evaluation of the project.

Jennifer B. Unger, Ph.D., is a Professor of Preventive Medicine at the University of Southern California Keck School of Medicine. Her research focuses on psychological, social, and cultural influences of substance use and other health risk behaviors among diverse adolescents and entertainmenteducation for health promotion. She also directs the Ph.D. program in Health Behavior Research at USC.



Latent class analysis (LCA): It is a person-centered analytic technique that allows for the identification of homogenous subgroups from a larger heterogenous population/sample.

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REFERENCES Almeida, J., Johnson, R. M., Matsumoto, A., & Godette, D. C. (2012). Substance use, generation and time in the United States: The modifying role of gender for immigrant urban adolescents. Social Science & Medicine, 75(12), 2069–2075. Auerbach, K. J., & Collins, L. M. (2006). A multidimensional developmental model of alcohol use during emerging adulthood. Journal of Studies on Alcohol, 67(6), 917–925. Austin, G., & Duerr, M. (2004). Guidebook for the California Healthy Kids Survey. Part I: Administration. 2004–2005 Edition. San Francisco: WestEd. Bal, D. G., Kizer, K. W., Felton, P. G., Mozar, H. N., & Niemeyer, D. (1990). Reducing tobacco consumption in California: Development of a statewide anti-tobacco use campaign. Journal of the American Medical Association, 264, 1570–1574. California Department of Public Health. (2012). Medical marijuana program. Retrieved March 2, 2012, from http://www. Program.aspx Centers for Disease Control and Prevention. (2011). 1991–2009 high school youth risk behavior survey data. Retrieved May 1, 2011, from Clark, P. I., Scarisbrick-Hauser, A., Gautam, S. P., & Wirk, S. J. (1999). Anti-tobacco socialization in homes of AfricanAmerican and white parents, and smoking and nonsmoking parents. Journal of Adolescent Health, 24(5), 329–339. Cleveland, M., Collins, L., Lanza, S. T., Greenberg, M., & Feinberg, M. (2010). Does individual risk moderate the effect of contextual-level protective factors? A latent class analysis of substance use. Journal of Prevention & Intervention in the Community, 38(3), 213–228. Connell, C., Gilreath, T., Aklin, W. M., & Brex, R. A. (2010). Social-ecological influences on patterns of substance use among non-metropolitan high school students. American Journal of Community Psychology, 45(1–2), 36–48. Connell, C., Gilreath, T., & Hansen, N. (2009). A multiprocess latent class analysis of the co-occurrence of substance use and sexual risk behavior among adolescents. Journal of Studies on Alcohol and Drugs, 70(6), 943–951. Cowling, D. W., & Yang, J. (2010). Smoking-attributable cancer mortality in California, 1979–2005. Tobacco Control, 19(1 Suppl.), i62–i67. De la Rosa, M. R. (2002). Acculturation and latino adolescents substance use: A research agenda for the future. Substance Use & Misuse, 37(4), 429–456. Eaton, D. K., Kann, L., Kinchen, S., Shanklin, S., Ross, J., Hawkins, J., et al. (2010). Youth risk behavior surveillance: United States, 2009. Morbidity and Mortality Weekly Report Surveillance Summary, 59(5), 1–142. Ettner, S., Huang, D., Evans, E., Ash, D., Hardy, M., Jourabchi, M., et al. (2006). Benefit-cost in the California treatment outcome project: Does substance abuse treatment “pay for itself”? Health Services Research, 41(1), 192–213. Gilreath, T. D., Connell, C. M., & Leventhal, A. M. (2012). Tobacco use and suicidality: Latent patterns of co-occurrence


among black adolescents. Nicotine & Tobacco Research, 14(8), 970–976. Grunbaum, J. A., Kann, L., Kinchen, S., Ross, J., Hawkins, J., Lowry, R., et al. (2004). Youth risk behavior surveillance: United States, 2003 (Abridged). Journal of School Health, 74(8), 307–324. Irwin, C. E., Jr., Burg, S. J., & Cart, C. U. (2002). America’s adolescents: Where have we been, where are we going? Journal of Adolescent Health, 31(6, 1 Suppl.), 91–121. Jessor, R. (1991). Risk behavior in adolescence: A psychosocial framework for understanding and action. Journal of Adolescent Health, 12(8), 597–605. Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2006). Monitoring the future national survey results on drug use, 1975–2005: Volume I, secondary school students (NIH publication no. 06-5883). Bethesda, MD: National Institute on Drug Abuse. Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2012). Monitoring the future national results on adolescent drug use: Overview of key findings, 2011. Ann Arbor, MI: The University of Michigan. Kuehn, B. M. (2011). Teen marijuana use on the rise. Journal of the American Medical Association, 305(3), 242. Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671–694. Lanza, S. T., Rhoades, B. L., Greenberg, M. T., & Cox, M. (2011). Modeling multiple risks during infancy to predict quality of the caregiving environment: Contributions of a personcentered approach. Infant Behavior and Development, 34(3), 390–406. Lubke, G. H., & Muth´en, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21–39. Magidson, J., & Vermunt, J. K. (2002). Latent class models for clustering: A comparison with K-Means. Canadian Journal of Marketing Research, 20, 37–44. McCutcheon, A. (1987). Latent class analysis. Beverly Hills, CA: Sage Publications. Miller, T. R., Levy, D. T., Spicer, R. S., & Taylor, D. M. (2006). Societal costs of underage drinking. Journal of Studies on Alcohol, 67(4), 519–528. Newcomb, M. D., Scheier, L. M., & Bentler, P. M. (1997). Effects of adolescent drug use on adult mental health: A prospective study of a community sample. In G. A. Marlatt & G. R. VandenBos (Eds.), Addictive behaviors: Readings on etiology, prevention, and treatment (pp. 169–211). Washington, DC: American Psychological Association. Nylund, K. L., Asparouhov, T., & Muth´en, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569. Siegel, M., Mowery, P. D., Pechacek, T. P., Strauss, W. J., Schooley, M. W., Merritt, R. K., et al. (2000). Trends in adult cigarette smoking in California compared with the rest of the United States, 1978–1994. American Journal of Public Health, 90(3), 372–379. Squeglia, L., Jacobus, J., & Tapert, S. (2009). The influence of substance use on adolescent brain development. Clinical Electroencephalography and Neuroscience, 40(1), 31–38. Sullivan, C., Childs, K., & O’Connell, D. (2010). Adolescent risk behavior subgroups: An empirical assessment. Journal of Youth and Adolescence, 39(5), 541–562.



Subst Use Misuse Downloaded from by on 08/23/13 For personal use only.

U.S. Census Bureau. (2012). State and county quick facts. Retrieved February 06, 2012, from http://quickfacts.census. gov/qfd/states/06000.html. USDHHS. (2004). The health consequences of smoking: A report of the Surgeon General. Rockville, MD: Centers for Disease Control, Office on Smoking and Health.

Wallace, J. M., Brown, T. N., Bachman, J. G., & Laveist, T. A. (2003). The influence of race and religion on abstinence from alcohol, cigarettes and marijuana among adolescents. Journal of Studies on Alcohol, 64(6), 843–848.

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