Using a factor mixture modeling approach in alcohol dependence in a general population sample

Abstract

Alcohol dependence (AD) is a complex and heterogeneous disorder. The identification of more homogeneous subgroups of individuals with drinking problems and the refinement of the diagnostic criteria are inter-related research goals. They have the potential to improve our knowledge of etiology and treatment effects, and to assist in the identification of risk factors or specific genetic factors. Mixture modeling has advantages over traditional modeling that focuses on either the dimensional or categorical latent structure. The mixture modeling combines both latent class and latent trait models, but has not been widely applied in substance use research. The goal of the present study is to assess whether the AD criteria in the population could be better characterized by a continuous dimension, a few discrete subgroups, or a combination of the two. More than seven thousand participants were recruited from the population-based Virginia Twin Registry, and were interviewed to obtain DSM-IV (Diagnostic and Statistical Manual of Mental Disorder, version IV) symptoms and diagnosis of AD. We applied factor analysis, latent class analysis, and factor mixture models for symptom items based on the DSM-IV criteria.Our results showed that a mixture model with one factor and three classes for both genders fit well. The three classes were a non-problem drinking group and severe and moderate drinking problem groups. By contrast, models constrained to conform to DSM-IV diagnostic criteria were rejected by model fitting indices providing empirical evidence for heterogeneity in the AD diagnosis. Classification analysis showed different characteristics across subgroups, including alcohol-caused behavioral problems, comorbid disorders, age at onset for alcohol-related milestones, and personality. Clinically, the expanded classification of AD may aid in identifying suitable treatments, interventions and additional sources of comorbidity based on these more homogenous subgroups of alcohol use problems.

Using Factor Mixture Models to Evaluate the Type A/B Classification of Alcohol Use Disorders in a Heterogeneous Treatment Sample

“…A two-class (low endorsement vs. high endorsement for abuse and dependence criteria), single factor model (i.e., single dimension within each class) was found in the first application of this type of model to DSM-IV abuse and dependence criteria (Muthén, 2006). The two latent classes were differentiated primarily by probability of impairment in social and work activities and did not correspond to the DSM-IV alcohol abuse and dependence categories, which is also consistent with Kuo et al (2008) and McBride et al (2013) who found similar dimensionality with 3-classes.…”

The effect of sample selection on the distinction between alcohol abuse and dependence

“…Based on the URS, the parallel shift of classes with respect to criterion endorsement probabilities indicates that classes differ in terms of quantity (i.e., severity), rather than quality (i.e., patterns). This is in line with previous LCA studies on alcohol (Bucholz et al, 1996;Kuo, Aggen, Prescott, Kendler, & Neale, 2008;Muthén, 2006) and other substance use disorders (Agrawal, Lynskey, Madden, Bucholz, & Heath, 2007;Chung & Martin, 2005;Grant et al, 2006). Based on the RS, however, a qualitative interaction between abuse, dependence and class membership emerged.…”

Greater Activation in Left Hemisphere Language‐Related Regions During Simple Judgment Tasks Among Substance‐Dependent Patients in Treatment for Alcoholism

“…The severity of their alcoholism, co-morbid psychiatric disorders, and abuse of substances in addition to alcohol is similar to the more severe subtypes of alcohol dependent individuals identified by latent class analyses of interviews with large epidemiological samples (Kuo et al, 2008;Moss et al, 2007). According to Moss and colleagues analysis, the subtypes similar to our subjects account for about 30% of individuals meeting DSM-IV criteria for alcohol dependence in a population-based household survey.…”