Substance Use & Misuse, 50:403–413, 2015 C 2015 Informa Healthcare USA, Inc. Copyright ISSN: 1082-6084 print / 1532-2491 online DOI: 10.3109/10826084.2014.984850
ORIGINAL ARTICLE
A Typology of Drug Selling Among Young Adults in the United States Michael G. Vaughn1 , Christopher P. Salas-Wright2 , Matt DeLisi3 , Jeffrey J. Shook4 and Lauren Terzis1
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1
School of Social Work, Saint Louis University, Saint Louis, Missouri, USA; 2 School of Social Work, The University of Texas at Austin, Austin, Texas, USA; 3 Department of Sociology, Center for the Study of Violence, Iowa State University, Ames, Iowa, USA; 4 The School of Social Work, University of Pittsburgh, Pittsburgh, Pennsylvania, USA steeply over the past 30 years (Caulkins & Chandler, 2006; Mauer & King, 2007). These increases have been attributed to “War on Drugs” policies that have largely entangled low level users and sellers and not major traffickers (Mauer & King, 2007). Several studies have found that drug sellers are not as homogenous as previously believed. That is, studies have found that drug sellers include friends selling to other friends, and episodic users who exchange to support their own habits, and in general do not fit preconceived images and media portrayals (e.g., Coomber, 2010; Gallupe, Bouchard, & Caulkins, 2011; Hammersley, 2011; Tarter, Kirisci, Mezzich, & Patton, 2011). Despite the War on Drugs and stereotypical views of drug sellers, generalizable research on the characteristics of drug sellers derived from national probability samples is lacking. This is important because the policies and practices surrounding the response to drug selling have not been informed by representative data that reflect personbased characteristics of drug sellers. In addition, there are too few studies of drug selling during early adulthood. Early adulthood, defined here as between the ages of 18–25, is a high offending period and also a critical time for the development of problems associated with substance use disorders (Chen & Kandel, 1995; Huesmann, Dubow, & Boxer, 2009). Early adulthood is also characterized by increased independence and economic instability (Arnett, 2005). Thus, a better empirical picture into drug selling during this period is valuable. Investigations of drug selling have focused more on youth rather than young adults. Data collected from youth drug sellers have found that drug selling is associated with a wide range of risky and delinquent behaviors (Li, Stanton, Feigelman, Black, & Romer, 1994; Little & Steinberg, 2006; Magyar, Edens, Lilienfeld, Douglas, & Poythress, 2011; McCurley & Snyder, 2008; Shook, Vaughn, Goodkind, & Johnson, 2011; Shook, Vaughn, & Salas-Wright, 2013). Unsurprisingly, findings garnered
Background: Although studies have found that young adults who sell drugs are more likely to be involved in risky behaviors than those who do not sell drugs, there has been relatively little research that has explored heterogeneity among young adults who sell drugs. Methods: Using a pooled sample of 18 to 25 year olds from the National Survey on Drug Use and Health (2006–2010) who report past-year drug selling (N = 5,373), this study employs latent profile analysis to specify latent groups and assess the correlates of group membership. Results: Findings indicate substantial differences among young adults who sell drugs. In particular, the analysis found four groups of drug sellers: normative (49.6%), club drug users (23.6%), polysubstance users (16.0%), and criminal offenders (10.8%). Club drug users were characterized by high levels of ecstasy and hallucinogen use, polysubstance users were more likely to be depressed and anxious, White and female than the other groups. Criminal offenders were overwhelmingly male and more likely to be comprised of African-Americans and Hispanics. Conclusions: Results indicate that drug selling in early adulthood varies substantially. Contrary to media and popular notions most drug sellers are not involved in crime and polysubstance using drug sellers are in clear need of mental health services. Further, most drug sellers in this age range are White. Findings suggest that policy efforts that operate under the assumption of homogeneity of drug selling may be misguided. Keywords drug selling, drug dealing, young adults, emerging adulthood, drug use
INTRODUCTION
Arrests stemming from possession and trafficking of illegal substances have risen three-fold from 1980 to 2005 and incarceration for drug offenses have risen even more
Address correspondence to Michael G. Vaughn, School of Social Work, Saint Louis University, Saint Louis, MI 63103. E-mail: [email protected]
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from these studies reveal that youth who sell drugs are involved in behavioral patterns tied to involvement in the juvenile justice system and substance abuse. Lacking bonding to conventional social institutions, youthful drug sellers often affiliate themselves with others who hold the same unconventional values (Steinman, 2005). As such, peers and family members are an important source of drug procurement that facilitates a network of relationships with those who also engage in risky activities (Black & Ricardo, 1994; Floyd et al., 2010; Shook et al., 2011). In terms of gender, girls have been found to be involved in the drug trade, but in qualitatively different roles compared to boys (Shook et al., 2011). Prior research has also found that the rates of drug selling are similar among Whites and Blacks, however, the patterns of substance use and contextual factors associated with drug selling are divergent (Floyd et al., 2010). Whites are more influenced by their own personal use of drugs while socioeconomic deprivation is a stronger driver for drug selling among Blacks (Floyd et al., 2010). While valuable, these studies have tended to assume that adolescents who sell drugs are homogenous. That is, studies have largely compared drug selling youth and nonselling youth across a range of demographic and behavioral factors without attention to the latent structure and associated heterogeneity existing among drug sellers. However, one exception is a recent investigation of Shook and colleagues (2013) that explored the heterogeneity in a sample of over 3,000 adolescents who selfreported having sold illicit drugs in the past year. Results revealed three discrete groups—dabblers, delinquents, and externalizers. Dabblers comprised the largest proportion of adolescent drug sellers (38.0%) and reported low and episodic levels of drug selling. Delinquent drug sellers (36.0%) reported a higher frequency of selling drugs than dabblers. Externalizers (26.0%) reported the highest frequency of drug selling. Dabblers reported the lowest levels of illegal behavior among the three groups. Delinquents and externalizers were involved in illegal behavior at high and almost identical rates. Externalizers reported using a wide range of illicit drugs compared to the other two groups. Delinquents were younger than dabblers, as well as more likely to be youth of color and come from poorer families. Externalizers were found to exhibit high levels of school problems, sensation seeking, anxiety, parental conflict, and peer drug use. Given the heterogeneity found in this study, a natural extension is to build upon these findings during young adulthood. Aims of the Present Study
This article addresses three major research needs on the phenomenon of drug selling. First, the need for research on drug selling that is generalizable. Knowledge on drug selling has relied too heavily on high-risk samples or samples from disadvantaged communities, limiting the understanding of the entirety of drug selling phenomenon. Second, we focus on drug selling in early adulthood, a transition time when many early risk behaviors take hold, and a potent period for crimogenic behavior. Third, we explore
and specify the heterogeneity among young drug sellers. As mentioned, most quantitative investigations compare drug sellers to non-dealers and do not examine or specify how sellers vary. We employ data from the National Survey on Drug Use and Health (NSDUH), a nationally representative study focused on substance use and related behaviors. Data are pooled from respondents aged 18–25 who participated in the NSDUH from 2006 to 2010 producing a uniquely large sample of 5,373 individuals who reported selling drugs in the 12 months prior to their interview. In addition to addressing the gap in generalizable knowledge, the nature of this sample allows us to examine the prevalence of “active” drug selling, including a focus on frequency. Using latent variable modeling procedures, we examine drug selling and its correlates within a personcentered rather than variable-centered framework. METHOD Sample and Procedures
This study was based on public-use data collected between 2006 and 2010 as part of the National Survey on Drug Use and Health (NSDUH) (SAMHSA, 2011). The NSDUH was designed to provide population estimates of substance use and health-related behaviors in the U.S. general population. It utilizes multistage area probability sampling methods to select a representative sample of the U.S. civilian, non-institutionalized population aged 12 years or older for participation in the study. Multistage sampling designs commonly are used when attempting to provide nationally representative estimates. With respect to the NSDUH, all 50 states and the District of Columbia were employed. Study participants include household residents; residents of shelters, rooming houses, and group homes; and civilians residing on military bases. To improve the precision of behavioral and drug use estimates for subgroups, young adults aged 18 to 25 years were oversampled. NSDUH study participants were interviewed in private at their places of residence. Potential participants were assured that their names would not be recorded and that their responses would be kept strictly confidential. Participants were paid 30 dollars for their participation. The NSDUH interview utilizes a computer-assisted interviewing (CAI) methodology to increase the likelihood of valid respondent reports of illicit drug use and high-risk behaviors (SAMHSA, 2011). The CAI methodology included a combination of computer-assisted personal interviewing (CAPI) and audio computer-assisted self-interviewing (ACASI) methodologies. A more detailed description of the NSDUH sampling and data collection procedures is documented in greater detail elsewhere (SAMHSA, 2011). A total of 280,098 respondents aged 12 years or older completed the survey between 2006 and 2010. Weighted response rates for these years were approximately 90% for household screening and 75% for interviewing (SAMHSA, 2011). Each independent, crosssectional NSDUH sample was considered representative
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DRUG SELLING AMONG YOUNG ADULTS IN THE UNITED STATES
of the U.S. general population aged 12 years or older. The current study restricted analyses to young adults between the ages of 18 and 25 who reported having sold illegal drugs one or more times during the previous 12 months (N = 5,373). The mean age of the sample was approximately 21 years old (SD = 1.8). The respondents were predominately male (73.9%) and the sample was unevenly distributed in terms of race/ethnicity. More than two-thirds of the respondents were White (67.6%) while only 14.2% were African-American and 13.1% Hispanic. The annual family income of the majority of the sample was less than $50,000 per year (64.7%) and roughly onethird of the respondents resided in households earning less than $20,000 per year (31.1%). As for those from families earning more than $50,000 per year, 12.8% earned between $50,000 and $74,000 and 22.5% were from families earning $75,000 or more per year. Measures
Variables Used to form Latent Classes This study made use of 20 variables measuring criminal justice involvement, chemical dependency/substance abuse, and illicit drug use in the identification of latent classes of young adult drug sellers. All measures examined young adult behavior during the previous 12 months and all items are self-reported. In addition, several demographic covariates were used in the modeling to enhance the specificity of identified subgroups. Criminal Justice Variables Five of the indicator variables measured recent history of arrest, including instances of arrest during the previous 12 months for public drunkenness, driving under the influence of alcohol, larceny, assault, and serious violent offense. Sample items included: “In the past 12 months, were you arrested and booked for aggravated assault, forcible rape, murder, homicide, or nonnegligent manslaughter?” and “In the past 12 months, were you arrested and booked for larceny or theft?” Two additional criminal justice variables measured if respondents had been on probation or parole during the previous 12 month period. For each of these items, young adults who responded affirmatively were coded as 1 while youth who reported no criminal justice involvement were coded as 0. Chemical Dependency/Substance Abuse Three additional indicator variables measured nicotine dependence and alcohol and marijuana abuse. Nicotine dependence was determined on the basis of the Nicotine Dependence Syndrome Scale (Shiffman, Waters, & Hickcox, 2004) and the Fagerstrom Test of Nicotine Dependence (Heatherton, Kozlowski, Frecker, & Fagerstrom, 1989). Respondents who met criteria for nicotine dependence for either measure were identified as nicotine dependent and coded as 1 while all other respondents were coded as 0. Alcohol and marijuana abuse were determined based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), 4th edition (American Psychiatric Association [APA], 1994). Respondents who met
405
DSM criteria for alcohol or marijuana abuse were coded as 1 and all other respondents coded as 0. Further information as to the precise operationalization of these three variables is provided in greater detail elsewhere (SAMHSA, 2011). Illicit Drug Use Beyond dependency and abuse, 10 indicator variables measured the use of illicit substances over the previous 12 month period. Substances included: cocaine, crack, ecstasy, hallucinogens, inhalants, tranquilizers, stimulants, methamphetamine, heroin, and Oxycontin. Young adult users of the aforementioned substances were identified by responding to questions about the frequency of the use of said substances. For each item, respondents who reported that they had not used these substances on any days were coded as 0 while youth who reported one or more instances of use were coded as 1. Demographic Covariates Finally, several key sociodemographic variables were also included as covariates to refine the validation of externalizing subgroups of young adults. The following variables were used: age, gender, race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and other [American Indian or Alaska Native, Asian, other Pacific Islander or Native Hawaiian, and persons reporting more than one race]), and total annual family income (less than $20,000, $20,000 to $49,999, $50,000 to $74,999, and $75,000 or more). Family income was ascertained by asking respondents: “Of these income groups, which category best represents your total combined family income during the previous calendar year?” Individual and Mental Health Correlates
Risk Propensity Risk propensity (α = 0.81) was calculated as the mean score of two ordinal items related to the frequency of enjoyment of participation in dangerous or risky behaviors. These items measured the frequency of respondent agreement with the following statements, “How often do you like to test yourself by doing something a little risky?” and “How often do you get a real kick out of doing things that are a little dangerous?” Both items had the response format of “never” (1), “seldom” (2), or “sometimes/always” (3). Substance Disapproval Substance disapproval was assessed by a single item measuring the degree of respondent disapproval of young adults trying cannabinoids. Respondents were queried: “How do you feel about adults trying marijuana or hashish once or twice?” Young adults who reported “neither approving nor disapproving” where coded as 0 and those who reported “somewhat” or “strongly” disapproving were coded as 1.
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Religious Salience Religious salience (α = 0.82) was assessed by three items measuring the degree to which respondents considered religious beliefs to be important to their life, decision making, and friendships. These three items were measured by asking respondents the degree to which they agreed with the following statements: “Your religious beliefs are a very important part of your life;” “Your religious beliefs influence how you make decisions in your life;” and “It is important to you that your friends share your religious beliefs.” All items had the response format of “strongly disagree” (1), “disagree” (2), “agree” (3), and “strongly agree” (4).
Next, having identified a latent profile solution, cases were assigned to classes based on the probability of membership as indicated by the model. Finally, this profile solution was, in turn, validated by means of using Chisquare and analysis of variance (ANOVA) tests to systematically compare sociodemographic, individual, and mental health differences between classes. Bonferroni posthoc tests were also used to assess mean differences as part of the ANOVA test. Statistical procedures were conducted using Stata 12.1SE survey data functions so as to adjust standard errors of estimates for complex survey sampling design effects including clustered multistage data (StataCorp, 2011).
High School Dropout High school dropout status (Yes = 1, No = 0) was assessed by means of two items. Respondents were queried as to their highest level of educational attainment and their current status as students. Respondents who had not completed high school and who were not currently enrolled in school were considered to be high school dropouts (N = 1,028, 17.3%).
RESULTS
Residential Instability Residential instability was measured by a single item querying the number of times young adults reported having moved in the previous 5 year period. Response options range from “none” (0) to “six or more times” (6). Mental Health Diagnoses Two measures (Yes = 1, No = 0) examined key indicators of psychological wellbeing: recent depression and recent anxiety. Determination of depression (N = 573, 10.5%) and anxiety (N = 508, 9.5%) were based on whether respondents had been informed by a doctor or medical professional that they met criteria either of these disorders during the previous 12 months. Statistical Analyses
Statistical analyses were carried out in three sequential steps. First, latent class analysis (LCA) was conducted in order to identify latent subgroups of drug sellers in the sample. LCA is a statistical procedure that assigns subjects to their most likely subgroups on the basis of observed data. In carrying out the LCA, a series of latent class models ranging from 1 to 5 classes was exR 4.5 software (Vermunt & amined using Latent GOLD Magidson, 2008). Several criteria were used such as the Bayesian Information Criterion (BIC), Akaike’s Information Criterion (AIC), Consistent Akaike’s Information Criterion (CAIC), and Log-likelihood. In the interpretation of these goodness-of-fit indicators, lower AIC, CAIC, and BIC values and higher log-likelihood values reflect better model fit. Beyond these quantitative indicators, the clarity and conceptual interpretability of various class solutions is also considered in the identification of the final model (Nylund, Asparouhov, & Muthen, 2007).
Comparative Prevalence of Externalizing Behaviors
Figure 1 presents the prevalence estimates for criminal justice involvement, chemical dependency/abuse, and illicit substance use for young adult drug sellers (N = 5,373) and young adults in the general population (N = 93,710) during the previous 12 months. Across the board, young adult drug sellers reported higher levels of criminal justice involvement, chemical dependency/substance abuse, and illicit substance use. Indeed, with the exception of nicotine dependence and alcohol abuse, the prevalence of the externalizing variables examined among nondrug sellers were all approximately around or below 5.0%. In terms of criminal justice involvement, the percentage of young adult drug sellers arrested for all behaviors examined was greater than that of non-drug sellers. Most notably, a particular marked difference was observed in terms of probation with 16.2% of drug sellers having been on probation in the last year compared to only 5.5% of non-drug sellers. As for chemical dependency and substance abuse, substantial differences could also be observed in terms of nicotine dependence (28.5% for drug sellers versus 12.3% for non-drug sellers), alcohol abuse (23.0% for drug sellers versus 9.5% for non-drug sellers), and marijuana abuse (11.0% for drug sellers versus 1.8% for non-drug sellers). A similar pattern of differential severity could be observed for the use of all illicit drugs, but particular noteworthy differences were observed in terms of cocaine use (33.4% for drug sellers versus 5.5% for non-drug sellers), hallucinogen use (39.7% for drug sellers versus 6.6% for non-drug sellers), and tranquilizer use (28.0% for drug sellers versus 5.0% for non-drug sellers). Overall, young adult drug sellers reported substantially greater involvement in the criminal justice system, greater levels of chemical dependency and substance abuse, and greater rates of illicit drug use. Identification of Latent Classes
The analysis of the goodness-of-fit statistics and conceptual clarity of the latent class models suggested that the four class solution was the optimal model. As illustrated in Table 1, the AIC, CAIC, and BIC values for the five class solution were slightly lower and the log-likelihood value slightly higher than the respective values for the four
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DRUG SELLING AMONG YOUNG ADULTS IN THE UNITED STATES
39.7
40
33.4
35
28.5
30
28.0 24.6
Prevalence
25
23.0
20
17.1
16.2
14.3
15
12.3 11.0 9.5
9.5
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10 4.9 5 1.6
5.5
5.1 1.5
3.6
4.0
0.9
1.2
5.5 4.3 1.9
1.4
6.1
5.0 3.7
2.8 0.6
6.6 5.3
0.7
4.5 3.3
1.7
2.1 0.8
0.5
0
Drug Sellers (N = 5,373)
Non-Drug Sellers ( N = 93,710)
FIGURE 1. Last year prevalence of 20 criminal justice system and substance use/abuse variables among drug selling young adults (18–25 years).
class solution. Nevertheless, these differences were minimal and, given the accelerated flattening of the changes between the four and five class solutions, suggest that the inclusion of an additional class beyond the fourth class would be superfluous. Moreover, the conceptual clarity and interpretability of the four class solution suggested that this class was also the optimal class. In sum, both the goodness-of-fit statistics and the conceptual clarity of the four class solution indicated that if four class solution was the best solution in terms of the identification of a statistically viable, sufficiently parsimonious, and substantively meaningful latent class solution.
TABLE 1. Fit indices for latent classes Class solution
BIC
AIC
CAIC
Loglikelihood
1 Class 2 Classes 3 Classes 4 Classes 5 Classes
77512.0 72008.2 70574.0 69661.6 68954.9
77380.2 71698.5 70086.4 68996.0 68111.5
77532.0 72055.2 70648.0 69762.6 69082.9
−38670.1 −35802.3 −34969.2 −34397.0 −33927.7
Note: AIC = Akaike’s Information Criterion, BIC = Bayesian Information Criterion, CAIC = Consistent Akaike’s Information Criterion.
As noted above, the four class model provided the greatest conceptual clarity and interpretability. Such clarity and interpretability were assessed by means of plotting the mean values of the 20 criminal justice, chemical dependency/substance abuse, and illicit drug use variables across each of the latent classes. As seen in Figure 2, the four class solution is comprised of a normative class (N = 2,664, 49.6%), a club drug users class (N = 1,268, 23.6%), a polysubstance users class (N = 862, 16.0%), and a criminal offenders class (N = 579, 10.8%). Figure 3 presents the frequency of drug selling across these four latent classes. The normative class is the largest class, comprising roughly 50% of all drug sellers, and is characterized as the class with the lowest overall involvement in the criminal justice system, chemical dependency/substance abuse, and illicit drug use. While just over one in four young adults in this class sold drugs routinely (e.g., 10 or more times) (27.9%), half of the members of the class (50.0%) had only sold drugs once or twice in the last year. The club drug users class represents roughly one-quarter of all drug selling young adults (23.6%) and is characterized by low levels of criminal justice involvement and exceedingly high levels of ecstasy (64.0%) and hallucinogen use (100.0%), as well as elevated levels of cocaine use (43.3%). In terms of frequency of drug selling, nearly two in five (39.8%) of the youth of this class routinely sold drugs. The
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100 90 80 70
Prevalence
60 50 40
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30 20 10 0
Class 1: Normave (49.58%)
Class 2: Club Drug Users (23.60%)
Class 3: Polysubstance Users (16.04%)
Class 4: Criminal Offenders (10.78%)
FIGURE 2. Last year prevalence of 20 criminal justice system and substance use/abuse variables across four latent classes among drug selling young adults (18–25 years).
polysubstance users class represents approximately one in six (16.0%) young adult drug sellers and is characterized primarily by elevated levels of nicotine dependence (53.4%), and consistently high levels of use of all illicit drugs examined in this study (between 23% and 85%). The polysubstance users class had the highest proportion of frequent drug selling youth (50.0%) and the lowest percentage of young adults selling drugs only one or two times per year (22.0%). Finally, the criminal offenders class, which was the smallest of all classes (10.8%), was characterized by very high levels of criminal justice involvement—ranging between 15.6% for larceny and 25.9% for assault—and relatively low-to-moderate levels of chemical dependency/substance abuse and illicit drug use. The criminal offenders class was relatively balanced between those who rarely sold drugs (39.0%) and those who routinely sold drugs (37.7%). In all, these four classes are readily distinguishable with differential characteristics in terms of participation in externalizing behaviors and variable frequencies in terms of drug selling. Sociodemographic and Psychosocial Characteristics of Latent Classes
Table 2 presents the percentages and confidence intervals of sociodemographic, individual, and mental health factors across the four latent classes along with the results of the Chi-square and ANOVA tests. All of the correspond-
ing tests of difference in this table were statistically significant at the p < .001 level with the exception of family income, which was significant at the p < .01 level. In terms of age, a greater proportion of young adults in all four latent classes tended to be on the younger end of young adulthood with the vast majority between the ages of 18 and 21 rather than 22 and 25. Notably, however, this difference was most pronounced among the club drug users class as 70.9% of class members were between the ages of 18 and 21 and only 29.1% were between the ages of 22 and 25. Along the lines of gender, a greater proportion of young adults in all four latent classes also tended to be male, but this was most strongly pronounced in the criminal offenders class in which 86.9% of all respondents were male. The class with the lowest proportion of male members was the polysubstance users class with 66.1% male and 33.9% female. Notable differences were also observed in terms of race and ethnicity. The polysubstance users class was disproportionately White (88.7%) and had exceedingly low levels of African-Americans (0.70%) and Hispanics (6.4%). The criminal offenders class, in contrast, had the highest concentration of African-American (28.0%) and Hispanic (20.2%) young adults of any of the classes. Fewer than half of all members of the criminal offenders class were White (44.3%). As for family income, less variability was observed in general, but the criminal offenders class was found to be the class with the highest
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DRUG SELLING AMONG YOUNG ADULTS IN THE UNITED STATES
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FIGURE 3. Last year frequency of drug selling across four latent classes of young adult drug sellers (18–25 years).
proportion of young adults from families earning less than $50,000 per year (70.5%) While the Chi-square and ANOVA tests for all for individual and mental health factors were significant, a variety of particularly noteworthy differences can be highlighted in Table 2. Levels of substance use disapproval were particularly low among the club drug users class (5.9%) and the polysubstance users class (7.3%) suggesting that very low percentages of members of the classes disapprove of low-level substance experimentation. Members of the club drug users and polysubstance users class also reported significantly higher mean levels of risk propensity and lower levels of religious salience compared to the normative and criminal offenders classes. In terms of dropout status, members of the club drug users class had the lowest level of high school dropouts (10.8%) and the criminal offenders class had very high levels of high school dropouts (37.9%). In terms of residential instability, the polysubstance users and the criminal offenders both reported a higher mean number of moves in the last 5 years compared to the normative and club drug users classes. Finally, in terms of mental health status, the polysubstance users stood out as having nearly two times the proportion of members with recent depression (19.3%) and anxiety (18.8%). DISCUSSION
The study sought to address three major research needs concerning drug selling. First, previous research has focused mainly on geographically circumscribed high-risk samples of drug sellers from disadvantaged communities, which has limited the understanding and true representation of the phenomenon. The current study utilized a na-
tional sample (NSDUH) that is specifically designed to provide a population estimate of substance use and healthrelated behaviors of the entire U.S. general population. This study was able to sample all types of drug sellers in early adulthood, not just those from disadvantaged communities. For example, the family income of young adult drug sellers that was $75,000 and above ranged from the lowest proportion of 19.6% (criminal offenders) to the highest at 24.9% (club-drug users). This shows that a substantial amount of drug sellers come from relatively higher income households than previously believed. With respect to our second aim pertaining to transitions to adulthood, results from this study found that a large majority of drug sellers across latent classes reported to be in their earlier years of young adulthood (i.e., 18–21 years of age). Importantly, this suggests an aging out of drug selling effect where young adults progress into their later years of young adulthood, their participation in drug selling decreases. Our final aim was to explore and specify the heterogeneity and advance a typology among drug sellers in early adulthood. Our results yielded evidence of four distinct latent classes: normative (49.6%), club-drug users (23.6%), polysubstance users (16.0%), and criminal offenders (10.8%). Study findings build on research suggesting that the assumption of homogeneity within drug users is incorrect (Phillips, 2012). Results also extend (Shook et al., 2013) investigation that explored the heterogeneity of drug selling among adolescents in the United States. Their analysis found three groups of adolescent drug dealers: dabblers, delinquents, and externalizers. Both investigations found a high prevalence of tobacco, alcohol, and marijuana use among drug sellers relative to the general population. In the current study, the normative and club drug
(58.3–63.3) (36.6–41.7) (71.4–75.7) (24.3–28.6) (58.0–63.2) (16.7–20.8) (13.5–17.6) (4.3–6.6) (27.5–32.1) (34.0–39.0) (10.8–14.0) (19.4–23.9) (0.68) (15.2–18.8) (0.82) (14.6–18.4) (1.8) (6.8–9.6) (6.1–8.9)
60.9 39.1
73.6 26.4
60.6 18.6 15.4 5.3
29.7 36.4 12.3 21.6
2.4 16.8 2.2 16.4 2.1
8.1 7.4
9.9 8.0
2.6 5.9 2.0 10.8 2.1
32.7 28.9 13.5 24.9
77.1 8.7 9.8 4.4
74.4 25.6
70.9 29.1
(7.9–12.4) (6.3–10.2)
(0.53) (4.4–7.9) (0.8) (9.0–13.0) (2.0)
(29.4–36.2) (25.8–32.2) (11.2–16.2) (21.8–28.3)
(73.8–80.1) (6.8–10.9) (7.8–12.4) (3.1–6.2)
(71.2–77.4) (22.6–28.8)
(67.5–74.1) (25.9–32.5)
% (CI) / Mean (SD)
% (CI) / Mean (SD)
19.3 18.8
2.7 7.3 2.0 17.1 2.6
32.9 28.4 15.2 23.5
88.7 0.7 6.4 4.2
66.1 33.9
60.9 39.0
(16.1–22.9) (15.6–22.6)
(0.5) (5.4–9.8) (0.8) (14.3–20.4) (2.06)
(28.9–37.2) (24.7–32.3) (12.1–18.8) (19.8–27.6)
(85.5–91.2) (0.4–1.3) (4.4–9.3) (2.8–6.1)
(61.8–70.0) (30.0–38.1)
(56.6–65.2) (34.8–43.4)
% (CI) / Mean (SD)
Class 3 (polysubstance users) N = 862 (16.0%)
9.6 7.9
2.4 20.5 2.3 37.9 2.6
31.1 39.5 9.8 19.6
44.3 28.0 20.2 7.4
86.9 13.1
61.5 38.5
(6.4–14.0) (5.2–11.9)
(0.7) (15.6–26.5) (0.8) (32.0–44.2) (2.0)
(26.0–36.6) (33.7–45.6) (6.9–13.9) (14.9–25.3)
(38.5–50.2) (22.7–34.1) (15.3–26.3) (5.1–10.6)
(82.6–90.2) (9.7–17.4)
(55.5–67.1) (32.9–44.5)
% (CI) / Mean (SD)
Class 4 (criminal offenders) N = 579 (10.8%)
Bonferroni’s test: a = Classes 1 and 2 are different; b = Classes 1 and 3 are different; c = Classes 1 and 4 are different; d = Classes 2 and 3 are different; e = Classes 2 and 4 are different; f = Classes 3 and 4 are different.
Demographic factors Age 18 to 21 22 to 25 Gender Male Female Race/Ethnicity White African-American Hispanic Other Family income < $20,000 $20,000–$49,000 $50,000–$74,000 > $75,000 Individual factors Risk propensity Substance disapproval Religious salience Dropout status Residential instability Mental health Depression Anxiety
Class 2 (club drug users) N = 1268 (23.6%)
Class 1 (normative) N = 2664 (49.6%)
TABLE 2. Univariate comparisons across four latent classes
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87.4 104.9
201.3
142.1
46.9
469.4
75.8
42.7
χ 2 value
19.5bcde
22.6abef
79.7abcef
F
ORIGINAL ARTICLE
A Typology of Drug Selling Among Young Adults in the United States Michael G. Vaughn1 , Christopher P. Salas-Wright2 , Matt DeLisi3 , Jeffrey J. Shook4 and Lauren Terzis1
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1
School of Social Work, Saint Louis University, Saint Louis, Missouri, USA; 2 School of Social Work, The University of Texas at Austin, Austin, Texas, USA; 3 Department of Sociology, Center for the Study of Violence, Iowa State University, Ames, Iowa, USA; 4 The School of Social Work, University of Pittsburgh, Pittsburgh, Pennsylvania, USA steeply over the past 30 years (Caulkins & Chandler, 2006; Mauer & King, 2007). These increases have been attributed to “War on Drugs” policies that have largely entangled low level users and sellers and not major traffickers (Mauer & King, 2007). Several studies have found that drug sellers are not as homogenous as previously believed. That is, studies have found that drug sellers include friends selling to other friends, and episodic users who exchange to support their own habits, and in general do not fit preconceived images and media portrayals (e.g., Coomber, 2010; Gallupe, Bouchard, & Caulkins, 2011; Hammersley, 2011; Tarter, Kirisci, Mezzich, & Patton, 2011). Despite the War on Drugs and stereotypical views of drug sellers, generalizable research on the characteristics of drug sellers derived from national probability samples is lacking. This is important because the policies and practices surrounding the response to drug selling have not been informed by representative data that reflect personbased characteristics of drug sellers. In addition, there are too few studies of drug selling during early adulthood. Early adulthood, defined here as between the ages of 18–25, is a high offending period and also a critical time for the development of problems associated with substance use disorders (Chen & Kandel, 1995; Huesmann, Dubow, & Boxer, 2009). Early adulthood is also characterized by increased independence and economic instability (Arnett, 2005). Thus, a better empirical picture into drug selling during this period is valuable. Investigations of drug selling have focused more on youth rather than young adults. Data collected from youth drug sellers have found that drug selling is associated with a wide range of risky and delinquent behaviors (Li, Stanton, Feigelman, Black, & Romer, 1994; Little & Steinberg, 2006; Magyar, Edens, Lilienfeld, Douglas, & Poythress, 2011; McCurley & Snyder, 2008; Shook, Vaughn, Goodkind, & Johnson, 2011; Shook, Vaughn, & Salas-Wright, 2013). Unsurprisingly, findings garnered
Background: Although studies have found that young adults who sell drugs are more likely to be involved in risky behaviors than those who do not sell drugs, there has been relatively little research that has explored heterogeneity among young adults who sell drugs. Methods: Using a pooled sample of 18 to 25 year olds from the National Survey on Drug Use and Health (2006–2010) who report past-year drug selling (N = 5,373), this study employs latent profile analysis to specify latent groups and assess the correlates of group membership. Results: Findings indicate substantial differences among young adults who sell drugs. In particular, the analysis found four groups of drug sellers: normative (49.6%), club drug users (23.6%), polysubstance users (16.0%), and criminal offenders (10.8%). Club drug users were characterized by high levels of ecstasy and hallucinogen use, polysubstance users were more likely to be depressed and anxious, White and female than the other groups. Criminal offenders were overwhelmingly male and more likely to be comprised of African-Americans and Hispanics. Conclusions: Results indicate that drug selling in early adulthood varies substantially. Contrary to media and popular notions most drug sellers are not involved in crime and polysubstance using drug sellers are in clear need of mental health services. Further, most drug sellers in this age range are White. Findings suggest that policy efforts that operate under the assumption of homogeneity of drug selling may be misguided. Keywords drug selling, drug dealing, young adults, emerging adulthood, drug use
INTRODUCTION
Arrests stemming from possession and trafficking of illegal substances have risen three-fold from 1980 to 2005 and incarceration for drug offenses have risen even more
Address correspondence to Michael G. Vaughn, School of Social Work, Saint Louis University, Saint Louis, MI 63103. E-mail: [email protected]
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from these studies reveal that youth who sell drugs are involved in behavioral patterns tied to involvement in the juvenile justice system and substance abuse. Lacking bonding to conventional social institutions, youthful drug sellers often affiliate themselves with others who hold the same unconventional values (Steinman, 2005). As such, peers and family members are an important source of drug procurement that facilitates a network of relationships with those who also engage in risky activities (Black & Ricardo, 1994; Floyd et al., 2010; Shook et al., 2011). In terms of gender, girls have been found to be involved in the drug trade, but in qualitatively different roles compared to boys (Shook et al., 2011). Prior research has also found that the rates of drug selling are similar among Whites and Blacks, however, the patterns of substance use and contextual factors associated with drug selling are divergent (Floyd et al., 2010). Whites are more influenced by their own personal use of drugs while socioeconomic deprivation is a stronger driver for drug selling among Blacks (Floyd et al., 2010). While valuable, these studies have tended to assume that adolescents who sell drugs are homogenous. That is, studies have largely compared drug selling youth and nonselling youth across a range of demographic and behavioral factors without attention to the latent structure and associated heterogeneity existing among drug sellers. However, one exception is a recent investigation of Shook and colleagues (2013) that explored the heterogeneity in a sample of over 3,000 adolescents who selfreported having sold illicit drugs in the past year. Results revealed three discrete groups—dabblers, delinquents, and externalizers. Dabblers comprised the largest proportion of adolescent drug sellers (38.0%) and reported low and episodic levels of drug selling. Delinquent drug sellers (36.0%) reported a higher frequency of selling drugs than dabblers. Externalizers (26.0%) reported the highest frequency of drug selling. Dabblers reported the lowest levels of illegal behavior among the three groups. Delinquents and externalizers were involved in illegal behavior at high and almost identical rates. Externalizers reported using a wide range of illicit drugs compared to the other two groups. Delinquents were younger than dabblers, as well as more likely to be youth of color and come from poorer families. Externalizers were found to exhibit high levels of school problems, sensation seeking, anxiety, parental conflict, and peer drug use. Given the heterogeneity found in this study, a natural extension is to build upon these findings during young adulthood. Aims of the Present Study
This article addresses three major research needs on the phenomenon of drug selling. First, the need for research on drug selling that is generalizable. Knowledge on drug selling has relied too heavily on high-risk samples or samples from disadvantaged communities, limiting the understanding of the entirety of drug selling phenomenon. Second, we focus on drug selling in early adulthood, a transition time when many early risk behaviors take hold, and a potent period for crimogenic behavior. Third, we explore
and specify the heterogeneity among young drug sellers. As mentioned, most quantitative investigations compare drug sellers to non-dealers and do not examine or specify how sellers vary. We employ data from the National Survey on Drug Use and Health (NSDUH), a nationally representative study focused on substance use and related behaviors. Data are pooled from respondents aged 18–25 who participated in the NSDUH from 2006 to 2010 producing a uniquely large sample of 5,373 individuals who reported selling drugs in the 12 months prior to their interview. In addition to addressing the gap in generalizable knowledge, the nature of this sample allows us to examine the prevalence of “active” drug selling, including a focus on frequency. Using latent variable modeling procedures, we examine drug selling and its correlates within a personcentered rather than variable-centered framework. METHOD Sample and Procedures
This study was based on public-use data collected between 2006 and 2010 as part of the National Survey on Drug Use and Health (NSDUH) (SAMHSA, 2011). The NSDUH was designed to provide population estimates of substance use and health-related behaviors in the U.S. general population. It utilizes multistage area probability sampling methods to select a representative sample of the U.S. civilian, non-institutionalized population aged 12 years or older for participation in the study. Multistage sampling designs commonly are used when attempting to provide nationally representative estimates. With respect to the NSDUH, all 50 states and the District of Columbia were employed. Study participants include household residents; residents of shelters, rooming houses, and group homes; and civilians residing on military bases. To improve the precision of behavioral and drug use estimates for subgroups, young adults aged 18 to 25 years were oversampled. NSDUH study participants were interviewed in private at their places of residence. Potential participants were assured that their names would not be recorded and that their responses would be kept strictly confidential. Participants were paid 30 dollars for their participation. The NSDUH interview utilizes a computer-assisted interviewing (CAI) methodology to increase the likelihood of valid respondent reports of illicit drug use and high-risk behaviors (SAMHSA, 2011). The CAI methodology included a combination of computer-assisted personal interviewing (CAPI) and audio computer-assisted self-interviewing (ACASI) methodologies. A more detailed description of the NSDUH sampling and data collection procedures is documented in greater detail elsewhere (SAMHSA, 2011). A total of 280,098 respondents aged 12 years or older completed the survey between 2006 and 2010. Weighted response rates for these years were approximately 90% for household screening and 75% for interviewing (SAMHSA, 2011). Each independent, crosssectional NSDUH sample was considered representative
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DRUG SELLING AMONG YOUNG ADULTS IN THE UNITED STATES
of the U.S. general population aged 12 years or older. The current study restricted analyses to young adults between the ages of 18 and 25 who reported having sold illegal drugs one or more times during the previous 12 months (N = 5,373). The mean age of the sample was approximately 21 years old (SD = 1.8). The respondents were predominately male (73.9%) and the sample was unevenly distributed in terms of race/ethnicity. More than two-thirds of the respondents were White (67.6%) while only 14.2% were African-American and 13.1% Hispanic. The annual family income of the majority of the sample was less than $50,000 per year (64.7%) and roughly onethird of the respondents resided in households earning less than $20,000 per year (31.1%). As for those from families earning more than $50,000 per year, 12.8% earned between $50,000 and $74,000 and 22.5% were from families earning $75,000 or more per year. Measures
Variables Used to form Latent Classes This study made use of 20 variables measuring criminal justice involvement, chemical dependency/substance abuse, and illicit drug use in the identification of latent classes of young adult drug sellers. All measures examined young adult behavior during the previous 12 months and all items are self-reported. In addition, several demographic covariates were used in the modeling to enhance the specificity of identified subgroups. Criminal Justice Variables Five of the indicator variables measured recent history of arrest, including instances of arrest during the previous 12 months for public drunkenness, driving under the influence of alcohol, larceny, assault, and serious violent offense. Sample items included: “In the past 12 months, were you arrested and booked for aggravated assault, forcible rape, murder, homicide, or nonnegligent manslaughter?” and “In the past 12 months, were you arrested and booked for larceny or theft?” Two additional criminal justice variables measured if respondents had been on probation or parole during the previous 12 month period. For each of these items, young adults who responded affirmatively were coded as 1 while youth who reported no criminal justice involvement were coded as 0. Chemical Dependency/Substance Abuse Three additional indicator variables measured nicotine dependence and alcohol and marijuana abuse. Nicotine dependence was determined on the basis of the Nicotine Dependence Syndrome Scale (Shiffman, Waters, & Hickcox, 2004) and the Fagerstrom Test of Nicotine Dependence (Heatherton, Kozlowski, Frecker, & Fagerstrom, 1989). Respondents who met criteria for nicotine dependence for either measure were identified as nicotine dependent and coded as 1 while all other respondents were coded as 0. Alcohol and marijuana abuse were determined based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), 4th edition (American Psychiatric Association [APA], 1994). Respondents who met
405
DSM criteria for alcohol or marijuana abuse were coded as 1 and all other respondents coded as 0. Further information as to the precise operationalization of these three variables is provided in greater detail elsewhere (SAMHSA, 2011). Illicit Drug Use Beyond dependency and abuse, 10 indicator variables measured the use of illicit substances over the previous 12 month period. Substances included: cocaine, crack, ecstasy, hallucinogens, inhalants, tranquilizers, stimulants, methamphetamine, heroin, and Oxycontin. Young adult users of the aforementioned substances were identified by responding to questions about the frequency of the use of said substances. For each item, respondents who reported that they had not used these substances on any days were coded as 0 while youth who reported one or more instances of use were coded as 1. Demographic Covariates Finally, several key sociodemographic variables were also included as covariates to refine the validation of externalizing subgroups of young adults. The following variables were used: age, gender, race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and other [American Indian or Alaska Native, Asian, other Pacific Islander or Native Hawaiian, and persons reporting more than one race]), and total annual family income (less than $20,000, $20,000 to $49,999, $50,000 to $74,999, and $75,000 or more). Family income was ascertained by asking respondents: “Of these income groups, which category best represents your total combined family income during the previous calendar year?” Individual and Mental Health Correlates
Risk Propensity Risk propensity (α = 0.81) was calculated as the mean score of two ordinal items related to the frequency of enjoyment of participation in dangerous or risky behaviors. These items measured the frequency of respondent agreement with the following statements, “How often do you like to test yourself by doing something a little risky?” and “How often do you get a real kick out of doing things that are a little dangerous?” Both items had the response format of “never” (1), “seldom” (2), or “sometimes/always” (3). Substance Disapproval Substance disapproval was assessed by a single item measuring the degree of respondent disapproval of young adults trying cannabinoids. Respondents were queried: “How do you feel about adults trying marijuana or hashish once or twice?” Young adults who reported “neither approving nor disapproving” where coded as 0 and those who reported “somewhat” or “strongly” disapproving were coded as 1.
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Religious Salience Religious salience (α = 0.82) was assessed by three items measuring the degree to which respondents considered religious beliefs to be important to their life, decision making, and friendships. These three items were measured by asking respondents the degree to which they agreed with the following statements: “Your religious beliefs are a very important part of your life;” “Your religious beliefs influence how you make decisions in your life;” and “It is important to you that your friends share your religious beliefs.” All items had the response format of “strongly disagree” (1), “disagree” (2), “agree” (3), and “strongly agree” (4).
Next, having identified a latent profile solution, cases were assigned to classes based on the probability of membership as indicated by the model. Finally, this profile solution was, in turn, validated by means of using Chisquare and analysis of variance (ANOVA) tests to systematically compare sociodemographic, individual, and mental health differences between classes. Bonferroni posthoc tests were also used to assess mean differences as part of the ANOVA test. Statistical procedures were conducted using Stata 12.1SE survey data functions so as to adjust standard errors of estimates for complex survey sampling design effects including clustered multistage data (StataCorp, 2011).
High School Dropout High school dropout status (Yes = 1, No = 0) was assessed by means of two items. Respondents were queried as to their highest level of educational attainment and their current status as students. Respondents who had not completed high school and who were not currently enrolled in school were considered to be high school dropouts (N = 1,028, 17.3%).
RESULTS
Residential Instability Residential instability was measured by a single item querying the number of times young adults reported having moved in the previous 5 year period. Response options range from “none” (0) to “six or more times” (6). Mental Health Diagnoses Two measures (Yes = 1, No = 0) examined key indicators of psychological wellbeing: recent depression and recent anxiety. Determination of depression (N = 573, 10.5%) and anxiety (N = 508, 9.5%) were based on whether respondents had been informed by a doctor or medical professional that they met criteria either of these disorders during the previous 12 months. Statistical Analyses
Statistical analyses were carried out in three sequential steps. First, latent class analysis (LCA) was conducted in order to identify latent subgroups of drug sellers in the sample. LCA is a statistical procedure that assigns subjects to their most likely subgroups on the basis of observed data. In carrying out the LCA, a series of latent class models ranging from 1 to 5 classes was exR 4.5 software (Vermunt & amined using Latent GOLD Magidson, 2008). Several criteria were used such as the Bayesian Information Criterion (BIC), Akaike’s Information Criterion (AIC), Consistent Akaike’s Information Criterion (CAIC), and Log-likelihood. In the interpretation of these goodness-of-fit indicators, lower AIC, CAIC, and BIC values and higher log-likelihood values reflect better model fit. Beyond these quantitative indicators, the clarity and conceptual interpretability of various class solutions is also considered in the identification of the final model (Nylund, Asparouhov, & Muthen, 2007).
Comparative Prevalence of Externalizing Behaviors
Figure 1 presents the prevalence estimates for criminal justice involvement, chemical dependency/abuse, and illicit substance use for young adult drug sellers (N = 5,373) and young adults in the general population (N = 93,710) during the previous 12 months. Across the board, young adult drug sellers reported higher levels of criminal justice involvement, chemical dependency/substance abuse, and illicit substance use. Indeed, with the exception of nicotine dependence and alcohol abuse, the prevalence of the externalizing variables examined among nondrug sellers were all approximately around or below 5.0%. In terms of criminal justice involvement, the percentage of young adult drug sellers arrested for all behaviors examined was greater than that of non-drug sellers. Most notably, a particular marked difference was observed in terms of probation with 16.2% of drug sellers having been on probation in the last year compared to only 5.5% of non-drug sellers. As for chemical dependency and substance abuse, substantial differences could also be observed in terms of nicotine dependence (28.5% for drug sellers versus 12.3% for non-drug sellers), alcohol abuse (23.0% for drug sellers versus 9.5% for non-drug sellers), and marijuana abuse (11.0% for drug sellers versus 1.8% for non-drug sellers). A similar pattern of differential severity could be observed for the use of all illicit drugs, but particular noteworthy differences were observed in terms of cocaine use (33.4% for drug sellers versus 5.5% for non-drug sellers), hallucinogen use (39.7% for drug sellers versus 6.6% for non-drug sellers), and tranquilizer use (28.0% for drug sellers versus 5.0% for non-drug sellers). Overall, young adult drug sellers reported substantially greater involvement in the criminal justice system, greater levels of chemical dependency and substance abuse, and greater rates of illicit drug use. Identification of Latent Classes
The analysis of the goodness-of-fit statistics and conceptual clarity of the latent class models suggested that the four class solution was the optimal model. As illustrated in Table 1, the AIC, CAIC, and BIC values for the five class solution were slightly lower and the log-likelihood value slightly higher than the respective values for the four
407
DRUG SELLING AMONG YOUNG ADULTS IN THE UNITED STATES
39.7
40
33.4
35
28.5
30
28.0 24.6
Prevalence
25
23.0
20
17.1
16.2
14.3
15
12.3 11.0 9.5
9.5
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10 4.9 5 1.6
5.5
5.1 1.5
3.6
4.0
0.9
1.2
5.5 4.3 1.9
1.4
6.1
5.0 3.7
2.8 0.6
6.6 5.3
0.7
4.5 3.3
1.7
2.1 0.8
0.5
0
Drug Sellers (N = 5,373)
Non-Drug Sellers ( N = 93,710)
FIGURE 1. Last year prevalence of 20 criminal justice system and substance use/abuse variables among drug selling young adults (18–25 years).
class solution. Nevertheless, these differences were minimal and, given the accelerated flattening of the changes between the four and five class solutions, suggest that the inclusion of an additional class beyond the fourth class would be superfluous. Moreover, the conceptual clarity and interpretability of the four class solution suggested that this class was also the optimal class. In sum, both the goodness-of-fit statistics and the conceptual clarity of the four class solution indicated that if four class solution was the best solution in terms of the identification of a statistically viable, sufficiently parsimonious, and substantively meaningful latent class solution.
TABLE 1. Fit indices for latent classes Class solution
BIC
AIC
CAIC
Loglikelihood
1 Class 2 Classes 3 Classes 4 Classes 5 Classes
77512.0 72008.2 70574.0 69661.6 68954.9
77380.2 71698.5 70086.4 68996.0 68111.5
77532.0 72055.2 70648.0 69762.6 69082.9
−38670.1 −35802.3 −34969.2 −34397.0 −33927.7
Note: AIC = Akaike’s Information Criterion, BIC = Bayesian Information Criterion, CAIC = Consistent Akaike’s Information Criterion.
As noted above, the four class model provided the greatest conceptual clarity and interpretability. Such clarity and interpretability were assessed by means of plotting the mean values of the 20 criminal justice, chemical dependency/substance abuse, and illicit drug use variables across each of the latent classes. As seen in Figure 2, the four class solution is comprised of a normative class (N = 2,664, 49.6%), a club drug users class (N = 1,268, 23.6%), a polysubstance users class (N = 862, 16.0%), and a criminal offenders class (N = 579, 10.8%). Figure 3 presents the frequency of drug selling across these four latent classes. The normative class is the largest class, comprising roughly 50% of all drug sellers, and is characterized as the class with the lowest overall involvement in the criminal justice system, chemical dependency/substance abuse, and illicit drug use. While just over one in four young adults in this class sold drugs routinely (e.g., 10 or more times) (27.9%), half of the members of the class (50.0%) had only sold drugs once or twice in the last year. The club drug users class represents roughly one-quarter of all drug selling young adults (23.6%) and is characterized by low levels of criminal justice involvement and exceedingly high levels of ecstasy (64.0%) and hallucinogen use (100.0%), as well as elevated levels of cocaine use (43.3%). In terms of frequency of drug selling, nearly two in five (39.8%) of the youth of this class routinely sold drugs. The
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M. G. VAUGHN ET AL.
100 90 80 70
Prevalence
60 50 40
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30 20 10 0
Class 1: Normave (49.58%)
Class 2: Club Drug Users (23.60%)
Class 3: Polysubstance Users (16.04%)
Class 4: Criminal Offenders (10.78%)
FIGURE 2. Last year prevalence of 20 criminal justice system and substance use/abuse variables across four latent classes among drug selling young adults (18–25 years).
polysubstance users class represents approximately one in six (16.0%) young adult drug sellers and is characterized primarily by elevated levels of nicotine dependence (53.4%), and consistently high levels of use of all illicit drugs examined in this study (between 23% and 85%). The polysubstance users class had the highest proportion of frequent drug selling youth (50.0%) and the lowest percentage of young adults selling drugs only one or two times per year (22.0%). Finally, the criminal offenders class, which was the smallest of all classes (10.8%), was characterized by very high levels of criminal justice involvement—ranging between 15.6% for larceny and 25.9% for assault—and relatively low-to-moderate levels of chemical dependency/substance abuse and illicit drug use. The criminal offenders class was relatively balanced between those who rarely sold drugs (39.0%) and those who routinely sold drugs (37.7%). In all, these four classes are readily distinguishable with differential characteristics in terms of participation in externalizing behaviors and variable frequencies in terms of drug selling. Sociodemographic and Psychosocial Characteristics of Latent Classes
Table 2 presents the percentages and confidence intervals of sociodemographic, individual, and mental health factors across the four latent classes along with the results of the Chi-square and ANOVA tests. All of the correspond-
ing tests of difference in this table were statistically significant at the p < .001 level with the exception of family income, which was significant at the p < .01 level. In terms of age, a greater proportion of young adults in all four latent classes tended to be on the younger end of young adulthood with the vast majority between the ages of 18 and 21 rather than 22 and 25. Notably, however, this difference was most pronounced among the club drug users class as 70.9% of class members were between the ages of 18 and 21 and only 29.1% were between the ages of 22 and 25. Along the lines of gender, a greater proportion of young adults in all four latent classes also tended to be male, but this was most strongly pronounced in the criminal offenders class in which 86.9% of all respondents were male. The class with the lowest proportion of male members was the polysubstance users class with 66.1% male and 33.9% female. Notable differences were also observed in terms of race and ethnicity. The polysubstance users class was disproportionately White (88.7%) and had exceedingly low levels of African-Americans (0.70%) and Hispanics (6.4%). The criminal offenders class, in contrast, had the highest concentration of African-American (28.0%) and Hispanic (20.2%) young adults of any of the classes. Fewer than half of all members of the criminal offenders class were White (44.3%). As for family income, less variability was observed in general, but the criminal offenders class was found to be the class with the highest
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DRUG SELLING AMONG YOUNG ADULTS IN THE UNITED STATES
409
FIGURE 3. Last year frequency of drug selling across four latent classes of young adult drug sellers (18–25 years).
proportion of young adults from families earning less than $50,000 per year (70.5%) While the Chi-square and ANOVA tests for all for individual and mental health factors were significant, a variety of particularly noteworthy differences can be highlighted in Table 2. Levels of substance use disapproval were particularly low among the club drug users class (5.9%) and the polysubstance users class (7.3%) suggesting that very low percentages of members of the classes disapprove of low-level substance experimentation. Members of the club drug users and polysubstance users class also reported significantly higher mean levels of risk propensity and lower levels of religious salience compared to the normative and criminal offenders classes. In terms of dropout status, members of the club drug users class had the lowest level of high school dropouts (10.8%) and the criminal offenders class had very high levels of high school dropouts (37.9%). In terms of residential instability, the polysubstance users and the criminal offenders both reported a higher mean number of moves in the last 5 years compared to the normative and club drug users classes. Finally, in terms of mental health status, the polysubstance users stood out as having nearly two times the proportion of members with recent depression (19.3%) and anxiety (18.8%). DISCUSSION
The study sought to address three major research needs concerning drug selling. First, previous research has focused mainly on geographically circumscribed high-risk samples of drug sellers from disadvantaged communities, which has limited the understanding and true representation of the phenomenon. The current study utilized a na-
tional sample (NSDUH) that is specifically designed to provide a population estimate of substance use and healthrelated behaviors of the entire U.S. general population. This study was able to sample all types of drug sellers in early adulthood, not just those from disadvantaged communities. For example, the family income of young adult drug sellers that was $75,000 and above ranged from the lowest proportion of 19.6% (criminal offenders) to the highest at 24.9% (club-drug users). This shows that a substantial amount of drug sellers come from relatively higher income households than previously believed. With respect to our second aim pertaining to transitions to adulthood, results from this study found that a large majority of drug sellers across latent classes reported to be in their earlier years of young adulthood (i.e., 18–21 years of age). Importantly, this suggests an aging out of drug selling effect where young adults progress into their later years of young adulthood, their participation in drug selling decreases. Our final aim was to explore and specify the heterogeneity and advance a typology among drug sellers in early adulthood. Our results yielded evidence of four distinct latent classes: normative (49.6%), club-drug users (23.6%), polysubstance users (16.0%), and criminal offenders (10.8%). Study findings build on research suggesting that the assumption of homogeneity within drug users is incorrect (Phillips, 2012). Results also extend (Shook et al., 2013) investigation that explored the heterogeneity of drug selling among adolescents in the United States. Their analysis found three groups of adolescent drug dealers: dabblers, delinquents, and externalizers. Both investigations found a high prevalence of tobacco, alcohol, and marijuana use among drug sellers relative to the general population. In the current study, the normative and club drug
(58.3–63.3) (36.6–41.7) (71.4–75.7) (24.3–28.6) (58.0–63.2) (16.7–20.8) (13.5–17.6) (4.3–6.6) (27.5–32.1) (34.0–39.0) (10.8–14.0) (19.4–23.9) (0.68) (15.2–18.8) (0.82) (14.6–18.4) (1.8) (6.8–9.6) (6.1–8.9)
60.9 39.1
73.6 26.4
60.6 18.6 15.4 5.3
29.7 36.4 12.3 21.6
2.4 16.8 2.2 16.4 2.1
8.1 7.4
9.9 8.0
2.6 5.9 2.0 10.8 2.1
32.7 28.9 13.5 24.9
77.1 8.7 9.8 4.4
74.4 25.6
70.9 29.1
(7.9–12.4) (6.3–10.2)
(0.53) (4.4–7.9) (0.8) (9.0–13.0) (2.0)
(29.4–36.2) (25.8–32.2) (11.2–16.2) (21.8–28.3)
(73.8–80.1) (6.8–10.9) (7.8–12.4) (3.1–6.2)
(71.2–77.4) (22.6–28.8)
(67.5–74.1) (25.9–32.5)
% (CI) / Mean (SD)
% (CI) / Mean (SD)
19.3 18.8
2.7 7.3 2.0 17.1 2.6
32.9 28.4 15.2 23.5
88.7 0.7 6.4 4.2
66.1 33.9
60.9 39.0
(16.1–22.9) (15.6–22.6)
(0.5) (5.4–9.8) (0.8) (14.3–20.4) (2.06)
(28.9–37.2) (24.7–32.3) (12.1–18.8) (19.8–27.6)
(85.5–91.2) (0.4–1.3) (4.4–9.3) (2.8–6.1)
(61.8–70.0) (30.0–38.1)
(56.6–65.2) (34.8–43.4)
% (CI) / Mean (SD)
Class 3 (polysubstance users) N = 862 (16.0%)
9.6 7.9
2.4 20.5 2.3 37.9 2.6
31.1 39.5 9.8 19.6
44.3 28.0 20.2 7.4
86.9 13.1
61.5 38.5
(6.4–14.0) (5.2–11.9)
(0.7) (15.6–26.5) (0.8) (32.0–44.2) (2.0)
(26.0–36.6) (33.7–45.6) (6.9–13.9) (14.9–25.3)
(38.5–50.2) (22.7–34.1) (15.3–26.3) (5.1–10.6)
(82.6–90.2) (9.7–17.4)
(55.5–67.1) (32.9–44.5)
% (CI) / Mean (SD)
Class 4 (criminal offenders) N = 579 (10.8%)
Bonferroni’s test: a = Classes 1 and 2 are different; b = Classes 1 and 3 are different; c = Classes 1 and 4 are different; d = Classes 2 and 3 are different; e = Classes 2 and 4 are different; f = Classes 3 and 4 are different.
Demographic factors Age 18 to 21 22 to 25 Gender Male Female Race/Ethnicity White African-American Hispanic Other Family income < $20,000 $20,000–$49,000 $50,000–$74,000 > $75,000 Individual factors Risk propensity Substance disapproval Religious salience Dropout status Residential instability Mental health Depression Anxiety
Class 2 (club drug users) N = 1268 (23.6%)
Class 1 (normative) N = 2664 (49.6%)
TABLE 2. Univariate comparisons across four latent classes
Subst Use Misuse Downloaded from informahealthcare.com by Tulane University on 01/31/15 For personal use only.
87.4 104.9
201.3
142.1
46.9
469.4
75.8
42.7
χ 2 value
19.5bcde
22.6abef
79.7abcef
F
