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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Subst Abuse Treat. Author manuscript; available in PMC Jun 1, 2011.
Published in final edited form as:
PMCID: PMC2844882
Using a latent variable approach to inform gender and racial/ethnic differences in cocaine dependence: A National Drug Abuse Treatment Clinical Trials Network study
Li-Tzy Wu, Sc.D.,a* Jeng-Jong Pan, Ph.D.,b Dan G. Blazer, M.D., Ph.D.,a Betty Tai, Ph.D.,c Maxine L. Stitzer, Ph.D.,d and George E. Woody, M.D.e
aDepartment of Psychiatry and Behavioral Sciences, School of Medicine, Duke University Medical Center, Duke Clinical Research Institute, Durham, NC 27710, USA
bVeterans Health Administration, Washington, DC 20024, USA
cNational Institute on Drug Abuse, Bethesda, MD 20892, USA
dDepartment of Psychiatry and Behavioral Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD 20892, USA
eDepartment of Psychiatry, School of Medicine, University of Pennsylvania and Treatment Research Institute, Philadelphia, PA 19106, USA
*1Corresponding author: Dr. Li-Tzy Wu, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Duke Clinical Research Institute, Duke University Medical Center Box 3419, Durham, NC 27710, USA. Tel: 1-919-668-6067; fax: 1-919-668-5418; litzy.wu/at/, litzywu/at/
This study applies a latent variable approach to examine gender and racial/ethnic differences in cocaine dependence, determine the presence of differential item functioning (DIF) or item-response bias to diagnostic questions of cocaine dependence, and explore the effects of DIF on the predictor analysis of cocaine dependence. The analysis sample included 682 cocaine users enrolled in two national multisite studies of the National Drug Abuse Treatment Clinical Trials Network (CTN). Participants were recruited from 14 community-based substance abuse treatment programs associated with the CTN, including 6 methadone and 8 outpatient non-methadone programs. Factor and multiple indicators–multiple causes (MIMIC) procedures evaluated the latent continuum of cocaine dependence and its correlates. MIMIC analysis showed that men exhibited lower odds of cocaine dependence than women (regression coefficient, β=−0.34), controlling for the effects of DIF, years of cocaine use, addiction treatment history, comorbid drug dependence diagnoses, and treatment setting. There were no racial/ethnic differences in cocaine dependence; however, DIF by race/ethnicity was noted. Within the context of multiple community-based addiction treatment settings, women were more likely than men to exhibit cocaine dependence. Addiction treatment research needs to further evaluate gender related differences in drug dependence in treatment entry and to investigate how these differences may affect study participation, retention, and treatment response in order to better serve this population.
Keywords: CTN Data Share, Clinical trials network, Cocaine dependence, Differential item functioning, Multiple indicators–multiple causes model
After marijuana, cocaine is the second most commonly used illicit drug in the United States (Substance Abuse and Mental Health Services Administration [SAMHSA], 2008). In 2007, an estimated 5.7 million Americans used cocaine in the past year, and approximately 1.6 million Americans had a current (past-year) cocaine use disorder as defined by Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV (SAMHSA, 2008). Cocaine also is one of the substances most frequently cited by Americans seeking substance abuse treatment with at least 0.8 million Americans seeking treatment for cocaine use in 2007 (SAMHSA, 2008). When taking into account the number of users of specific drugs, those using cocaine or heroin are most likely to enter substance abuse treatment (SAMHSA, 2008).
Gender and race/ethnicity are among the most critical variables to study among those seeking treatment due to their paramount importance in demonstrating health disparities in drug addiction and implications for etiological research, diagnosis, intervention, and health policy (American Psychiatric Association [APA], 2000; US Department of Health and Human Services, 2000). Evidence on gender and racial/ethnic differences in cocaine dependence, however, has been mixed. Using data from the 1990–1992 National Comorbidity Survey, Wagner and Anthony (2007) found that there were no gender differences in the risk for developing cocaine dependence within the first year of cocaine use, but that this risk declined more rapidly among women in the next 3 years after first use. On the other hand, by analyzing a subsample of recent-onset cocaine users (first use of cocaine within 24 months) aged ≥ 12 years from the 2000–2001 National Surveys on Drug Use and Health, O’Brien and Anthony (2005) reported that women (compared with men) and Blacks (compared with Whites) had an increased likelihood for experiencing cocaine dependence. However, others did not find gender or racial/ethnic differences in cocaine dependence among recent-onset users in the 1995–1998 National Household Surveys on Drug Use (Reboussin & Anthony, 2006).
To date, little is known about gender and racial/ethnic differences in cocaine dependence among treatment-seeking cocaine users. Results from prior studies are limited. Studies of demographic or drug use characteristics typically rely on a small sample of cocaine-using patients (e.g., N <200) who often were recruited from a single treatment program (Denier et al., 1991; Hernandez-Avila et al., 2004; Jones et al., 2004; Kosten et al., 1993; Lejuez et al., 2007; Weiss et al., 1997; Wong et al., 2002). Similarly, findings on demographic variations in drug use characteristics are often constrained by the descriptive nature of the analyses and a relatively low degree of generalizability. While none of these studies directly tested or reported demographic disparities in the probability of cocaine dependence among cocaine users, they suggested that women who seek substance abuse treatment exhibit a more severe pattern of drug use-related social, medical, or psychiatric problems than their male counterparts (Denier et al., 1991; Hernandez-Avila et al., 2004; Weiss et al., 1997).
Recently, Najavits and Lester (2008) examined gender differences in demographic and drug use characteristics among 2,376 treatment-seeking cocaine-dependent patients who participated in the National Institute on Drug Abuse Collaborative Cocaine Treatment Study (NCCTS). Compared with men, women were less educated and report more drug use-related problems (medical, family/social, employment, and psychiatric). These findings, however, were based on descriptive analyses, and gender or racial/ethnic differences in the probability of cocaine dependence were not available (Najavits & Lester, 2008).
In light of the lack of information on gender and racial/ethnic differences in cocaine dependence and their implications for treatment planning and interventions, this study examines gender and race/ethnicity differences in a sample of treatment seeking cocaine users from two multisite trials conducted within the National Drug Abuse Treatment Clinical Trials Network (CTN). The CTN studies have three strengths. First, cocaine users were recruited from 14 community-based substance abuse treatment programs (greater diversity and generalizability), and were all assessed by an identical set of measures (Peirce et al., 2006; Petry et al., 2005). Second, gender and racial/ethnic differences in current (past-year) DSM-IV cocaine dependence were tested by examining the conditional probability of cocaine dependence among past-year cocaine users. Third, we applied a latent variable regression approach (multiple indicators–multiple causes; MIMIC) to elucidate gender and racial/ethnic differences for the likelihood of cocaine dependence and the effects of differential item functioning (DIF) or item-response bias on the diagnostic assessment for cocaine dependence (e.g., see Blanco et al., 2007; Chen & Anthony, 2003; Wu et al., 2009a; Wu et al., 2009b).
DIF occurs when cocaine users with an equivalent level of dependence respond differentially to certain diagnostic items as a function of group membership such as gender (e.g., Wu et al., 2009a; Wu et al., 2009b). In the MIMIC model, diagnostic items shown to have DIF by gender suggest that these items have a different severity (threshold) for women than for men (e.g., possibly due to different meanings or interpretations of diagnostic questions). The presence of DIF could lead to inaccurate comparisons across groups with similar diagnoses, as well as on prevalence rates, risk factors, and treatment outcome of the measured diagnoses (e.g., Chen & Anthony, 2003; McHorney & Fleishman, 2006; Wu et al., 2009b). Analysis of DIF has timely and important implications for the emerging DSM-V because little is presently known about the effects of DIF by gender and race/ethnicity on predictor analysis for cocaine dependence, and failure to consider possible gender-related (possibly due to gender-specific biology or socialization) or racial/ethnic differences (possibly due to language and cultural differences in health beliefs or attitudes) in manifestations and expressions of symptoms may distort estimates of diagnoses and related risk factor analyses (e.g., Grant & Weissman, 2007; Room, 2006). Thus, evaluating and understanding DIF by gender and race/ethnicity for diagnostic criteria is fundamental to developing less biased estimates of a diagnosis for women and minority groups.
This study explores these issues by examining (1) whether gender or racial/ethnic differences in cocaine dependence exist controlling for potential effects of DIF, and (2) the effects of DIF in relation to gender, race/ethnicity, and comorbid substance dependence (alcohol, marijuana, amphetamines, and opioids) on the analysis of potential risk factors for cocaine dependence. The large and heterogeneous sample of trial participants also permits an exploration of whether the presence of drug dependence may distort cocaine users’ endorsement of dependence criteria, which has important implications for analyses of diagnostic data from treatment-seeking patients who frequently have comorbid drug dependence, and for informing diagnostic assessments of women and minority groups (e.g., Grant & Weissman, 2007; Room, 2006; Wu et al., 2009c).
2.1. Data source
Analyses were performed on public-use data sets from two multisite CTN studies that evaluated stimulant use outcomes in an abstinence-based contingency management intervention as an addition to usual care in non-methadone (non-MMT) (Petry et al., 2005) and methadone maintenance treatment (MMT) (Peirce et al., 2006). All programs were outpatient providers associated with CTN nodes in their geographic regions. Non-MMT participants were recruited from eight programs that did not administer methadone or other opioid agonists (Petry et al., 2005).Six of these programs were located in eastern, southeastern, or southwestern urban regions of the United States; one was in the suburban southeast and one in the rural southwest. Eligible patients were those who: (1) reported stimulant use within 2 weeks of study entry, (2) used stimulants within two weeks of entering a controlled environment (a detoxification unit, hospital, or correctional facility) and exited it within two weeks of study entry, or (3) submitted a stimulant-positive urine at treatment entry, (4) denied having or being in recovery from a gambling problem, and (5) demonstrated understanding of study procedures by correctly answering ≥ 80% of the questions on a quiz covering the requirements, risks, and benefits of participation in the parent study.
MMT participants were recruited from six programs located in urban areas in the northeastern, eastern, or southwestern United States (Peirce et al., 2006). Eligible participants included opioid-dependent patients who had: (1) been enrolled in MMT for a minimum of 30 days but not longer than 3 years, (2) submitted a stimulant-positive (cocaine or amphetamines) urine within 2 weeks of study enrollment (verified from clinic records), (3) denied having or being in recovery from a gambling problem, and (4) demonstrated understanding of study procedures by correctly answering ≥ 80% of the questions on a quiz covering the requirements, risks, and benefits of participation in the parent study.
2.2. Assessments
Social and demographic variables collected at study intake included age, gender, race/ethnicity, and years of education; substance use disorders were assessed by the DSM-IV Checklist (Wu et al., 2009b). All participants were asked about past-year use of five classes of substances: amphetamines, cocaine, opioids, alcohol, and marijuana. If participants reported use of any of these substances in the past year, they were asked about each of the seven DSM-IV dependence criteria associated with that substance (APA, 2000) and the DSM-IV Checklist assessed for dependence. Substance-specific dependence criteria were assessed and endorsing 3 or more of the 7 DSM-IV dependence criteria resulted in a dependence diagnosis: (1) tolerance; (2) withdrawal; (3) substance often taken in large amounts or for longer periods of time; (4) persistent desire or unsuccessful attempt to cut down or control use; (5) a great deal of time spent in activities necessary to get the substance; (6) important activities given up; and (7) continued substance use despite knowledge of having persistent or recurrent physical or psychological problems.
2.3. Data analyses
Analyses were based on data from 682 participants aged ≥ 18 years who reported cocaine use in the past year using the DSM-IV Checklist. We first examined the distributions of study variables and prevalence of cocaine dependence by gender and race/ethnicity. Next, factor analysis of binary data with the weighted least squares mean and variance adjusted (WLSMV) estimator was done using Mplus (Muthén & Muthén, 2007) to examine the unidimensionality (a one-factor model) of the cocaine dependence criteria, which is established by demonstrating that a one-factor model provides the most parsimonious fit to the data. We assessed the number of factors to be retained with the scree test (Cattell, 1996), the ratio of the first eigenvalue to the second eigenvalue, and variance explained by the first eigenvector (Wu et al., 2009b). Finally, MIMIC procedures in Mplus were applied to the 7 dependence criteria and covariates of cocaine dependence (age, gender, race/ethnicity, years of education, years of cocaine use, drug abuse treatment episodes, comorbid drug dependence, and treatment setting). Cocaine dependence was specified as a one-factor model in the MIMIC modeling as shown from factor analysis.
The MIMIC model incorporates the measurement part of the 7 dependence criteria (the latent dependence factor; cocaine dependence criteria) and a regression component of study covariates (the potential predictors of the latent dependence factor) (Wu et al., 2009a; Wu et al., 2009b; Wu et al., 2010). This model was extended into a structural equation model which allowed us to examine DIF (significant direct effects or potential item-response bias) of each cocaine dependence criterion item by each categorical covariate while simultaneously holding constant the level of cocaine dependence criteria. This model thus has advantages over the traditional regression model because MIMIC procedures take into account potential measurement errors (i.e, DIF) of the dependence measure and model it comprehensively as a continuous factor (increasing the statistical power). A traditional linear or logistic regression model cannot accommodate this feature.
To enhance the understanding of the effects of DIF on regression coeffcients of predictors for cocaine dependence, we report regression coefficients of covariates from the crude model (one covariate plus the measurement part of cocaine dependence criteria), adjusted regression model (multiple covariates plus the measurement part), and structural equation regression model (multiple covariates, the measurement part, and DIF). Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA) were all used to assess the fit of the MIMIC model. Values of TLI and CFI ≥ 0.95 (1 = perfect fit) and values of RMSEA ≤ 0.06 (the lower the value, the better the fit) indicate an excellent fit to the data (Browne & Cudeck, 1993; Hu & Bentler, 1999).
3.1. Characteristics of cocaine users (Table 1)
Compared with MMT patients, non-MMT patients were younger (mean age: 37 vs. 42 years) and more likely women (58% vs. 45%). There were no differences in race/ethnicity and years of education (mean: 11.8 years). The majority (74%) were members of non-White groups: Black, 53%; Hispanic, 15%; others, 6%.
Drug use and DSM–IV dependence
There were no differences in years of cocaine use (mean: 9.1 years) or prevalence of DSM–IV cocaine and amphetamine dependence by treatment setting. Notably, MMT patients had more episodes of prior drug abuse treatment (mean: 4.9 vs. 2.8 episodes) and a higher prevalence of opioid dependence (79% vs. 12%) than non-MMT patients, while non-MMT patients had a higher prevalence of alcohol dependence (42% vs. 12%) and marijuana dependence (20% vs. 8%). Note that opioid dependence was determined from the DSM–IV checklist, and that it reflected the status at study intake, not the status at admission to the methadone maintenance treatment program. Because of methadone treatment, some of the patients had no signs or symptoms of dependence and thus did not endorse 3 or more of the dependence criteria items.
Cocaine dependence by gender and race/ethnicity
Because gender and race/ethnicity were the primary covariates of interest, we determined their association with cocaine dependence. Overall, women had a higher prevalence of cocaine dependence than men (85% vs. 76%; χ2 test p <0.01); this finding was noted in both MMT (85% vs. 73%; χ2 test p <0.01) and non-MMT patients (84% vs. 78%; χ2 test p=0.064). There were no racial/ethnic differences in the prevalence of cocaine dependence overall or in either treatment setting.
Table 1
Table 1
Selected characteristics of outpatient cocaine users (N = 682)
3.2. Unidimensionality of the dependence construct (Table 2)
The prevalence of specific dependence criterion (ranging from 50% for Withdrawal to 79% for Inability to cut down) and their factor loading (ranging from 0.75 for Withdrawal to 0.92 for Time spent using cocaine) are summarized in Table 2. The scree test showed that a one-factor model fitted the seven criteria for cocaine dependence. Specifically, the first eigenvalue (5.24) and the ratio of the first-to-second eigenvalues was high (5.24/0.60=8.7); the first eigenvector explained 75% of the variance. All criterion items showed a good level of factor loadings (≥ 0.75) and the 7 dependence criteria exhibited a high level of internal consistency (Cronbach alpha coefficient = 0.86). Together, these findings provided support for the subsequent analysis of cocaine dependence criteria as a one-factor construct in the MIMIC model.
Table 2
Table 2
Prevalence and factor loadings of past-year DSM-IV cocaine dependence criteria (N = 682)
3.3. The MIMIC model of cocaine dependence syndrome (Figure 1)
Within the MIMIC model, cocaine dependence (a continuous index of severity) was regressed on covariates, and dependence criterion items showing DIF by covariates were regressed on the covariates in question. The MIMIC model (Figure 1) showed an excellent fit to the data (CFI=0.982, TLI=0.985, RMSEA=0.034)
Differential item functioning (Table 3)
This analysis suggested the presence of DIF by cocaine users’ racial/ethnic background and opioid dependence status. Holding constant the level of cocaine dependence, Hispanics were more likely than Whites to endorse Tolerance (regression coefficient, β=0.32), but less likely to endorse Continued use despite resulting problems (β=−0.34); Blacks were more likely than Whites to report Inability to cut down (β=0.41). Holding constant the level of cocaine dependence, cocaine users with opioid dependence were less likely than those without it to report Continued cocaine use despite resulting problems (β=−0.37). These DIF findings indicate different severity (threshold) levels of certain criterion items by race/ethnicity or opioid dependence status.
Table 3
Table 3
Significant differential item functioning (DIF) in the endorsements of cocaine dependence symptoms: Results of MIMIC modeling (N = 682)
Predictors of cocaine dependence (Table 4)
Holding constant DIF, older ages (β=−0.02), male gender (β=−0.34), amphetamine dependence (β=−0.53), and MMT setting (β=−0.31) were associated with decreased odds of cocaine dependence. On the other hand, years of cocaine use (β=0.03), prior drug abuse treatment (β=0.03), alcohol dependence (β=0.44), marijuana dependence (β=0.41), and opioid dependence (β=0.41) were associated with increased odds of cocaine dependence.
Table 4
Table 4
Regression coefficient estimates for predictors of cocaine dependence: Results of MIMIC models (N = 682)
The impact of DIF on regression coefficients of cocaine dependence
The impact of DIF on regression coefficients for cocaine dependence was explored by comparing the above-mentioned MIMIC model with one that excluded DIF (Table 4). A small change (14%) was observed on the estimated association (β) between opioid dependence status and cocaine dependence (β was changed from 0.36 to 0.41) after adjusting for DIF.
Figure 1
Figure 1
The MIMIC model of DSM-IV cocaine dependence (N=682).
Cocaine is the second most prevalent illicit drug used in the United States (SAMHSA, 2008). This study demonstrates a novel application of secondary data analysis of CTN public-use data (National Institute on Drug Abuse, 2009; It contributes new and important information on the assessment and the predictor analysis of dependence of patients seeking treatment for cocaine related problems by examining a large and geographically diverse sample of cocaine users recruited from 14 addiction treatment programs, investigating measurement bias (DIF) in assessing for cocaine dependence, applying a latent variable regression model to take into account measurement errors in the predictor analysis of cocaine dependence, and exploring the effects of DIF on diagnostic criteria. The latter has been an understudied and overlooked area of research for drug dependence, but has implications for improving the validity of analyzing self-reported diagnostic data.
MIMIC modeling revealed that (1) female cocaine users exhibited a higher likelihood of cocaine dependence than male cocaine users even after controlling for the potential confounding influences of age groups, race/ethnicity, years of cocaine use, addiction treatment history, comorbid drug dependence diagnoses, and treatment setting; (2) there was no evidence of gender-related item-response bias in the assessment of cocaine dependence; and (3) there was race/ethnicity-related DIF on the endorsement of cocaine dependence criteria. These findings have important implications not only for improving the analysis of self-reported data but also for the assessment of drug dependence, including the emerging DSM-V.
Traditionally, drug dependence is examined as a binary outcome. Such a categorical approach tends to have less statistical power and fails to consider potential measurement errors resulting from self-reported data of multiple diagnostic items. In fact, the dependence syndrome was originally conceptualized as a continuous, dimensional construct (Edwards & Gross, 1976). Consistent with prior studies of less geographically diverse or smaller samples (Bryant et al., 1991; Feingold & Rounsaville, 1995; Morgenstern et al., 1994), the results of factor analysis support our analysis of cocaine dependence as a single dimensional construct in the MIMIC model.
Additionally, MIMIC modeling of the criteria for cocaine dependence extends earlier research by utilizing response patterns from all dependence items and providing a flexible regression framework to statistically control for potential measurement errors from self-reports. Thus, results from the MIMIC model are less biased because the construct of the outcome of interest can be more properly modeled, and potential item-response biases from self-reports can be incorporated into the analysis.
Recently, results from the 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) suggest that women, Hispanics, and Blacks are more likely than men and Whites to report substance dependence but without abuse symptoms (Hasin et al., 2005; Hasin & Grant, 2004). The result is somewhat inconsistent with the DSM-IV’s specification because dependence is generally considered a more severe pattern of substance use than abuse (APA, 2000), and little is known about the reasons that may explain this finding. We utilized MIMIC procedures to evaluate whether there were potential item-response biases by gender and race/ethnicity in the assessment of drug dependence. Consistent with our recent research on alcohol and marijuana dependences (Wu et al., 2009b), we did not find gender-related DIF.
However, our analyses suggested the presence of DIF by cocaine users’ racial/ethnic backgrounds. A recent analysis of alcohol dependence data from NESARC also found evidence of DIF by race/ethnicity (Carle, 2009). Holding constant the cocaine dependence level, Hispanics were more likely than Whites to endorse Tolerance, but less likely to endorse Continued use despite resulting problems; Blacks were more likely than Whites to report Inability to cut down. Thus, Hispanics have a lower threshold than Whites to report Tolerance, but a higher threshold to report Continued use despite resulting problems; Blacks have a lower threshold than Whites to report Inability to cut down. This finding suggests the need to control for race/ethnicity statistically in the analysis of cocaine dependence, and to investigate reasons accounting for measurement invariance (e.g., racial/ethnic variations in the meaning or interpretation of diagnostic items) so as to reduce its potential confounding effects on assessing for cocaine dependence. Further, accurate estimates of cocaine dependence rely on the use of a sound measure. Study findings are, therefore, consistent with recommendations that cross-cultural research is needed for DSM-V to ensure that diagnostic criteria are equivalent or valid for different racial/ethnic groups (Escobar & Vega, 2006; Room, 2006).
Further, in addition to women, we found that young adults had increased odds of exhibiting a high level of cocaine dependence. Years of cocaine use, episodes of prior drug abuse treatment, as well as dependence on alcohol, marijuana, and opioids were all positively associated with cocaine dependence. These results are in line with prior findings suggesting that abusing one drug increases the likelihood of abusing others, which could be influenced through a shared vulnerability (Tsuang et al., 1998), greater opportunities for substance use (Wilcox et al., 2002), or direct effects from substance use (APA, 2000). For example, cocaine and opioids are often used together to enhance their subjective reinforcing effects (Leri et al., 2003), and alcohol is reportedly used by cocaine users to moderate the discomfort following cocaine use (Magura & Rosenblum, 2000).
Given that dependence on other substances was positively associated with the level of cocaine dependence in this study, women’s greater odds for exhibiting cocaine dependence is a cause of concern. Recently, we utilized latent class analysis to examine cocaine users in treatment, and found that women were more likely than men to be classified in the most severe subgroup that was characterized by reporting five or more dependence symptoms (Wu et al., 2009d). Another study on marijuana dependence among clinical patients also found greater odds of having it among women as compared with men (Wu et al., 2009b). Taken together, this and other studies suggest that treatment-seeking cocaine-using women may be disproportionately more severe in their substance use patterns and related problems than treatment-seeking men (Denier et al., 1991; Hernandez-Avila et al., 2004; Weiss et al., 1997; Wu et al., 2009d).
Lastly, amphetamine dependence in this study was associated with decreased odds of cocaine dependence, suggesting that dependence on both cocaine and amphetamines is infrequent. Our recent study of cocaine dependence among treat-seeking stimulant users also found a low rate (7%) of co-use of cocaine and amphetamines within a 12-month period among stimulant users (Wu et al., 2009d). It has been suggested that infrequent co-use of cocaine and amphetamines among regular stimulant users may be related to unique differences in demographic characteristics of users and pharmacological effects (see Wu et al., 2009d). For example, cocaine users were older and primarily Black, and amphetamine users were younger and predominantly White (Wu et al., 2009d). Differences between cocaine and amphetamines in their duration of effects, accessibility, and distribution networks may explain unique racial variations in the use of cocaine and amphetamines (Sexton et al., 2005).
4.1. Study limitations and strengths
These findings should be interpreted within the context of the following limitations. First, these findings are based on treatment-seeking drug users who participated in CTN trials. The study sample is not necessarily representative of all outpatient cocaine-using patients. Another limitation is reliance on participants’ self-reported information, which is subject to recall errors, exaggeration, or underreporting. Third, cocaine abuse was not examined due to the fact that dependence was assessed first, and abuse was not assessed among those who met the criteria for dependence. According to the DSM-IV (APA, 2000), abuse is not recognized among those with a dependence diagnosis. In this sample, 80% of cocaine users were classified as having cocaine dependence.
The present study also has several strengths. It extends prior studies of correlates of cocaine dependence by investigating a large, geographically diverse sample of users and employs MIMIC methods to understand item-level measurement errors in the risk factor analysis for cocaine dependence. Generalizability to clinical patients is improved because study participants were recruited from 14 major outpatient programs across the nation. These findings also demonstrate a novel use of CTN data to shed light on the quality of a drug dependence measure, the latent construct of cocaine dependence criteria, and predictors of cocaine dependence while simultaneously incorporating measurement errors into the analysis. Thus, this approach not only enhances our understanding of the drug-using population, but also provides important empirical information for future research on the quality and validity of diagnostic assessments for major diagnostic systems. Considering that substance use diagnoses are determined almost exclusively by self-reports and that the quality of self-report measures affects the validity of research and clinical findings, the need for the use of latent variable approaches to take into account measurement errors from self-reports is clear (Wu et al., 2009b).
4.2. Conclusions
Within the context of multiple community-based addiction treatment settings, this study demonstrates that women and young adults are more likely than men and older adults to exhibit cocaine dependence according to DSM-IV criteria, and that race/ethnicity is not associated with cocaine dependence. These findings suggest that addiction treatment research needs to evaluate further gender- and age-related differences at treatment entry, and investigate how these differences may affect study participation, retention, and treatment response in order to better serve these populations.
This research was supported mainly by the U.S. National Institute on Drug Abuse of the National Institutes of Health (HHSN271200522071C; PI, Dan G. Blazer) and in part by R01DA019623, R01DA019901, and R21DA027503 (PI, Li-Tzy Wu). Maxine L. Stitzer is principal investigator of the two CTN trials (5U10DA013034). George E. Woody was supported by K05DA017009 and U10DA013043 (PI, George E. Woody). The sponsoring agency had no further role in the study design and analysis, the writing of the report, or the decision to submit the paper for publication. The opinions expressed in this paper are solely those of the authors. We thank Amanda McMillan for her editorial assistance.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Ethical Approval: The Duke University Institutional Review Board exempted the present study from review because it used existing de-identified and public-use data.
Conflicts of Interest Disclosures: George E. Woody is a member of the RADARS post-marketing study external advisory group whose job is to assess abuse of prescription medications. Denver Health administers RADARS and 9 pharmaceutical companies currently support its work.
  • American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington, DC: American Psychiatric Publishing; 2000. text revision.
  • Blanco C, Harford TC, Nunes E, Grant B, Hasin D. The latent structure of marijuana and cocaine use disorders: results from the National Longitudinal Alcohol Epidemiologic Survey (NLAES) Drug and Alcohol Dependence. 2007;91:91–96. [PMC free article] [PubMed]
  • Browne MW, Cudeck R. Alternative ways of assessing model fit. In: Bollen KA, Long JS, editors. Testing structural equation models. Newbury Park, CA: Sage Publications; 1993. pp. 136–162.
  • Bryant KJ, Rounsaville BJ, Babor TF. Coherence of the dependence syndrome in cocaine users. British Journal of Addiction. 1991;86:1299–1310. [PubMed]
  • Carle AC. Cross-cultural invalidity of alcohol dependence measurement across Hispanics and Caucasians in 2001 and 2002. Addictive Behaviors. 2009;34:43–50. [PMC free article] [PubMed]
  • Cattell RB. The scree test for the number of factors. Multivariate Behavioral Research. 1996;1:245–276.
  • Chen CY, Anthony JC. Possible age-associated bias in reporting of clinical features of drug dependence: epidemiological evidence on adolescent-onset marijuana use. Addiction. 2003;98:71–82. [PubMed]
  • Denier CA, Thevos AK, Latham PK, Randall CL. Psychosocial and psychopathology differences in hospitalized male and female cocaine abusers: a retrospective chart review. Addictive Behaviors. 1991;16:489–496. [PubMed]
  • Escobar JI, Vega WA. Cultural issues and psychiatric diagnosis: providing a general background for considering substance use diagnoses. Addiction. 2006;101 Suppl 1:40–47. [PubMed]
  • Edwards G, Gross MM. Alcohol dependence: provisional description of a clinical syndrome. British Medical Journal. 1976;1:1058–1061. [PMC free article] [PubMed]
  • Feingold A, Rounsaville B. Construct validity of the dependence syndrome as measured by DSM-IV for different psychoactive substances. Addiction. 1995;90:1661–1669. [PubMed]
  • Grant BF, Weissman MM. Gender and the prevalence of psychiatric disorders. In: Narrow WE, First MB, Sirovatka PJ, Regier DA, editors. Age and gender considerations in psychiatric diagnosis, a research agenda for DSM–V. Virginia: American Psychiatric Association; 2007.
  • Hernandez-Avila CA, Rounsaville BJ, Kranzler HR. Opioid-, cannabis- and alcohol-dependent women show more rapid progression to substance abuse treatment. Drug and Alcohol Dependence. 2004;74:265–272. [PubMed]
  • Hasin DS, Grant BF. The co–occurrence of DSM–IV alcohol abuse in DSM-IV alcohol dependence: results of the National Epidemiologic Survey on Alcohol and Related Conditions on heterogeneity that differ by population subgroup. Archives of General Psychiatry. 2004;61:891–896. [PubMed]
  • Hasin DS, Hatzenbueler M, Smith S, Grant BF. Co–occurring DSM–IV drug abuse in DSM–IV drug dependence: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Drug and Alcohol Dependence. 2005;80:117–123. [PubMed]
  • Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling. 1999;6:1–55.
  • Jones HE, Johnson RE, Bigelow GE, Strain EC. Differences at treatment entry between opioid-dependent and cocaine-dependent males and females. Addictive Disorders and Their Treatment. 2004;3:110–121.
  • Kosten TA, Gawin FH, Kosten TR, Rounsaville BJ. Gender differences in cocaine use and treatment response. Journal of Substance Abuse Treatment. 1993;10:63–66. [PubMed]
  • Lejuez CW, Bornovalova MA, Reynolds EK, Daughters SB, Curtin JJ. Risk factors in the relationship between gender and crack/cocaine. Experimental and Clinical Psychopharmacology. 2007;15:165–175. [PMC free article] [PubMed]
  • Leri F, Bruneau J, Stewart J. Understanding polydrug use: review of heroin and cocaine co-use. Addiction. 2003;98:7–22. [PubMed]
  • Magura S, Rosenblum A. Modulating effect of alcohol use on cocaine use. Addictive Behaviors. 2000;25:117–122. [PubMed]
  • McHorney CA, Fleishman JA. Assessing and understanding measurement equivalence in health outcome measures. Issues for further quantitative and qualitative inquiry. Medical Care. 2006;44:S205–S210. [PubMed]
  • Morgenstern J, Langenbucher J, Labouvie EW. The generalizability of the dependence syndrome across substances: an examination of some properties of the proposed DSM-IV dependence criteria. Addiction. 1994;89:1105–1113. [PubMed]
  • Muthén BO, Muthén LK. Mplus: statistical analysis with latent variables (version 4.2.1) Los Angeles, CA: Muthén and Muthén, Inc.; 2007.
  • Najavits LM, Lester KM. Gender differences in cocaine dependence. Drug and Alcohol Dependence. 2008;97:190–194. [PMC free article] [PubMed]
  • National Institute on Drug Abuse. Clinical Trials Network Data Share, National Drug Abuse Treatment Clinical Trials Network. 2009. [Accessed August 18, 2009]. Available at:
  • O'Brien MS, Anthony JC. Risk of becoming cocaine dependent: epidemiological estimates for the United States, 2000–2001. Neuropsychopharmacology. 2005;30:1006–1018. [PubMed]
  • Peirce JM, Petry NM, Stitzer ML, Blaine J, Kellogg S, Satterfield F, Schwartz M, Krasnansky J, Pencer E, Silva-Vazquez L, Kirby KC, Royer-Malvestuto C, Roll JM, Cohen A, Copersino ML, Kolodner K, Li R. Effects of lower-cost incentives on stimulant abstinence in methadone maintenance treatment: a national drug abuse treatment clinical trials network study. Archives of General Psychiatry. 2006;63:201–208. [PubMed]
  • Petry NM, Peirce JM, Stitzer ML, Blaine J, Roll JM, Cohen A, Obert J, Killeen T, Saladin ME, Cowell M, Kirby KC, Sterling R, Royer-Malvestuto C, Hamilton J, Booth RE, Macdonald M, Liebert M, Rader L, Burns R, DiMaria J, Copersino M, Stabile PQ, Kolodner K, Li R. Effect of prize-based incentives on outcomes in stimulant abusers in outpatient psychosocial treatment programs: a National Drug Abuse Treatment Clinical Trials Network study. Archives of General Psychiatry. 2005;62:1148–1156. [PubMed]
  • Reboussin BA, Anthony JC. Is there epidemiological evidence to support the idea that a cocaine dependence syndrome emerges soon after onset of cocaine use? Neuropsychopharmacology. 2006;31:2055–2064. [PMC free article] [PubMed]
  • Room R. Taking account of cultural and societal influences on substance use diagnoses and criteria. Addiction. 2006;101 Suppl 1:31–39. [PubMed]
  • Sexton RL, Carlson RG, Siegal HA, Falck RS, Leukefeld C, Booth B. Barriers and pathways to diffusion of methamphetamine use among African Americans in the rural South: preliminary ethnographic findings. Journal of Ethnicity in Substance Abuse. 2005;4:77–103. [PubMed]
  • Substance Abuse and Mental Health Services Administration. Results from the 2007 National Survey on Drug Use and Health: National Findings. Rockville, MD: Office of Applied Studies, Substance Abuse and Mental Health Services; 2008.
  • Tsuang MT, Lyons MJ, Meyer JM, Doyle T, Eisen SA, Goldberg J, True W, Lin N, Toomey R, Eaves L. Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Archives of General Psychiatry. 1998;55:967–972. [PubMed]
  • U.S. Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. 2nd ed. Washington, DC: U.S. Government Printing Office; 2000.
  • Wagner FA, Anthony JC. Male-female differences in the risk of progression from first use to dependence upon cannabis, cocaine, and alcohol. Drug and Alcohol Dependence. 2007;86:191–198. [PubMed]
  • Weiss RD, Martinez-Raga J, Griffin ML, Greenfield SF, Hufford C. Gender differences in cocaine dependent patients: a 6 month follow-up study. Drug and Alcohol Dependence. 1997;44:35–40. [PubMed]
  • Wilcox HC, Wagner FA, Anthony JC. Exposure opportunity as a mechanism linking youth marijuana use to hallucinogen use. Drug and Alcohol Dependence. 2002;66:127–135. [PubMed]
  • Wong CJ, Badger GJ, Sigmon SC, Higgins ST. Examining possible gender differences among cocaine-dependent outpatients. Experimental and Clinical Psychopharmacology. 2002;10:316–323. [PubMed]
  • Wu LT, Ringwalt CL, Yang C, Reeve BB, Pan JJ, Blazer DG. Construct and differential item functioning in the assessment of prescription opioid use disorders among American adolescents. Journal of the American Academy of Child and Adolescent Psychiatry. 2009a;48:563–572. [PMC free article] [PubMed]
  • Wu LT, Pan JJ, Blazer DG, Tai B, Stitzer ML, Brooner RK, Woody GE, Patkar AA, Blaine JD. An item response theory modeling of alcohol and marijuana dependences: a National Drug Abuse Treatment Clinical Trials Network study. Journal of Studies on Alcohol and Drugs. 2009b;70:414–425. [PubMed]
  • Wu LT, Pan JJ, Blazer DG, Tai B, Brooner RK, Stitzer ML, Patkar AA, Blaine JD. The construct and measurement equivalence of cocaine and opioid dependences: a National Drug Abuse Treatment Clinical Trials Network (CTN) study. Drug and Alcohol Dependence. 2009c;103:114–123. [PMC free article] [PubMed]
  • Wu LT, Blazer DG, Patkar AA, Stitzer ML, Wakim PG, Brooner RK. Heterogeneity of stimulant dependence: a national drug abuse treatment clinical trials network study. American Journal on Addictions. 2009d;18:206–218. [PMC free article] [PubMed]
  • Wu LT, Pan JJ, Yang C, Reeve BB, Blazer DG. An item response theory analysis of DSM-IV criteria for hallucinogen use disorders in adolescents. Addictive Behaviors. 2010;35(3):273–277. [PMC free article] [PubMed]