Classification and short-term course of DSM-IV cannabis, hallucinogen, cocaine, and opioid disorders in treated adolescents

Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, PA 15213, USA.
Journal of Consulting and Clinical Psychology (Impact Factor: 4.85). 01/2006; 73(6):995-1004. DOI: 10.1037/0022-006X.73.6.995
Source: PubMed


This study examined the latent class structure of Diagnostic and Statistical Manual of Mental Disorders (text rev.; DSM-IV; American Psychiatric Association, 2000) symptoms used to diagnose cannabis, hallucinogen, cocaine, and opiate disorders among 501 adolescents recruited from addictions treatment. Latent class results were compared with the DSM-IV categories of abuse and dependence, and latent transition analysis (LTA) was used to examine changes in symptom severity over a 1-year follow-up. Although 2- and 3-class solutions provided the best fit to the data (2-class: hallucinogens, cocaine, opioids; 3-class: cannabis), 3-class solutions provided more substantive results and were emphasized in analyses. There was good agreement between latent classes and DSM-IV diagnosis. LTA suggested greater likelihood of transitioning to a less severe class at 1 year for all 4 drugs; in- and outpatients differed in pattern of change.

1 Read
  • Source
    • "Latent variable analyses including latent class analyses (LCA), factor analyses and item response theory analyses (IRT) have been used to address the psychometric properties of cannabis abuse and dependence (Helzer et al., 2007). In both population-based (Grant et al., 2006) and treated adolescents, (Chung & Martin, 2005), LCA identified classes of cannabis users based largely on severity. Investigators using data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) (Agrawal & Lynskey, 2007), male Virginia twins (Gillespie, Neale, Prescott, Aggen & Kendler, 2007) and the Australian general population (Teesson, Lynskey, Manor & Baillie, 2002) showed that 1-and 2-factor models corresponding to cannabis dependence and abuse fit the data well. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Genetic research on substance use disorders usually defines phenotypes as a binary diagnosis, resulting in a loss of information if the disorder is inherently dimensional. The DSM-IV criteria for drug dependence were based on a theoretically dimensional (linear) model. Considerable investigation has been conducted on DSM-IV alcohol criteria, but less is known about the dimensionality of DSM-IV cannabis criteria for abuse and dependence. The aim of this study is to assess whether DSM-IV cannabis dependence (including withdrawal) and abuse criteria fit a linear measure of severity and whether a consumption criterion adds linearly to severity. Participants were 8172 in the National Epidemiologic Survey on Alcohol and Related Conditions who had ever used cannabis. Wald statistics were used to test whether categorical, dimensional or hybrid forms best fit the data. We examined the following as criterion sets: (1) dependence; (2) dependence and abuse; and (3) dependence, abuse and frequency of use. Validating variables included family history of drug problems, early onset of cannabis use, and antisocial personality disorder. For cannabis dependence, no evidence was found for categorical or hybrid models; Wald tests indicated that models representing the seven DSM-IV dependence criteria as a linear severity measure best described the association between the criteria and validating variables. However, significant differences from linearity occurred after adding the four cannabis abuse criteria (p=0.03) and the use indicator (p=0.01) for family history and antisocial personality disorder. With ample power to detect non-linearity, cannabis dependence was shown to form an underlying continuum of severity. However, adding abuse criteria, with and without a measure of consumption, resulted in a model that differed significantly from linearity for two of the three validating variables.
    Addictive behaviors 11/2010; 35(11):961-9. DOI:10.1016/j.addbeh.2010.06.011 · 2.76 Impact Factor
  • Source
    • "Latent transition analysis is a longitudinal extension of LCA and a special type of Markov Chain modeling (Kaplan, 2008). This methodology models the movement of individuals into different latent classes over time, and has been used as an empirical method for examining how heterogeneity and symptom severity change over time in other DSM-IV disorders (e.g., Chung & Martin, 2005). One unique feature of this model is that it allows for testing moderators of change over the course of treatment. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The purpose of the study was to explore heterogeneity and differential treatment outcome among a sample of patients with binge eating disorder (BED). A latent class analysis was conducted with 205 treatment-seeking, overweight or obese individuals with BED randomized to interpersonal psychotherapy (IPT), behavioral weight loss (BWL), or guided self-help based on cognitive behavioral therapy (CBTgsh). A latent transition analysis tested the predictive validity of the latent class analysis model. A 4-class model yielded the best overall fit to the data. Class 1 was characterized by a lower mean body mass index (BMI) and increased physical activity. Individuals in Class 2 reported the most binge eating, shape and weight concerns, compensatory behaviors, and negative affect. Class 3 patients reported similar binge eating frequencies to Class 2, with lower levels of exercise or compensation. Class 4 was characterized by the highest average BMI, the most overeating episodes, fewer binge episodes, and an absence of compensatory behaviors. Classes 1 and 3 had the highest and lowest percentage of individuals with a past eating disorder diagnosis, respectively. The latent transition analysis found a higher probability of remission from binge eating among those receiving IPT in Class 2 and CBTgsh in Class 3. The latent class analysis identified 4 distinct classes using baseline measures of eating disorder and depressive symptoms, body weight, and physical activity. Implications of the observed differential treatment response are discussed.
    Journal of Consulting and Clinical Psychology 10/2010; 78(5):681-90. DOI:10.1037/a0019735 · 4.85 Impact Factor
  • Source
    • "Adolescent and adult studies using latent class analysis, a personcentered data analytic approach that identifies subgroups of individuals with similar symptom profiles, have consistently found classes that differ in the total number of symptoms rather than the type of symptoms (e.g., abuse vs. dependence classes) for alcohol (Bucholz, Heath, & Madden, 2000; Bucholz et al., 1996; Chung & Martin, 2001; Muthen, 2006), cannabis (J. D. Grant, Scherrer, Neuman, Todorov, Price, & Bucholz, 2006), and cocaine and opiates (Chung & Martin, 2005a), despite the fact that this analytic technique is well suited to detect discrete substance problem subtypes. When cross-classified, latent classes and DSM—IV diagnoses show a significant level of discordance (Chung & Martin, 2001, 2005a). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This article reviews literature on the validity and performance characteristics of the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) diagnostic criteria for substance use disorders (SUDs) and recommends changes in these criteria that should be considered for the next edition of the DSM (DSM-V). Substantial data indicate that DSM-IV substance abuse and substance dependence are not distinct categories and that SUD criteria are best modeled as reflecting a unidimensional continuum of substance-problem severity. The conceptually and empirically problematic substance abuse diagnosis should be abandoned in the DSM-V, with substance dependence defined by a single set of criteria. Data also indicate that various individual SUD criteria should be revised, dropped, or considered for inclusion in the DSM-V. The DSM-V should provide a framework that allows the integration of categorical and dimensional approaches to diagnosis. Important areas for further research are noted.
    Journal of Abnormal Psychology 09/2008; 117(3):561-75. DOI:10.1037/0021-843X.117.3.561 · 4.86 Impact Factor
Show more