Empirical derived AUD sub types in the US general population: A latent class analysis

Casey, Martina; Adamson, Gary; Stringer, Maurice

doi:10.1016/j.addbeh.2013.06.022

Cited by 18 publications

(30 citation statements)

References 22 publications

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“…Although many subtypes are possible due to different combinations of abuse and dependence criteria, the number that actually occur is lower[141,358]. Latent class analysis was a popular method to search for categories or subtypes[140,359-363], but these generally identified categories based on severity, as did taxometric analyses[364,365]. …”

Section: Major Findingsmentioning

confidence: 99%

The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) Waves 1 and 2: review and summary of findings

Hasin

Grant

2015

Soc Psychiatry Psychiatr Epidemiol

386

293

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Purpose The NESARC, a “third generation” psychiatric epidemiologic survey that integrated detailed measures of alcohol and drug use and problems, has been the data source for over >850 publications. A comprehensive review of NESARC findings and their implications is lacking. Method NESARC was a survey of 43,093 participants that covered alcohol, drug and psychiatric disorders, risk factors, and consequences. Wave 1 of the NESARC was conducted in 2001-2002. Three years later, Wave 2 follow-up re-interviews were conducted with 34,653 of the original participants. Scopus and Pubmed were used to search for NESARC papers, which were sorted into topic areas and summarized. Result The most common disorders were alcohol and post-traumatic stress disorders, and major depression. Females had more internalizing disorders and males had more externalizing disorders, although the preponderance of males with alcohol disorders (the “gender gap”) is less pronounced than it was in previous decades. A race/ethnic “paradox” (lower risk among disadvantaged minorities than whites) remains unexplained. Younger participants had higher risk for substance and personality disorders, but not unipolar depressive or anxiety disorders. Psychiatric comorbidity was extensive and often formed latent trans-diagnostic domains. Since 1991-1992, risk for marijuana and prescription drug disorders increased, while smoking decreased, although smoking decreases were less pronounced among those with comorbidity. A nexus of comorbidity, social support, and stress predicted transitions in diagnostic status between Waves 1 and 2. Childhood maltreatment predicted psychopathology. Alcohol and drug use disorders were seldom treated; attitudinal barriers (little perceived need, perceived alcoholism stigma, pessimism about efficacy) were more important in predicting non-treatment than financial barriers. Conclusions Understanding comorbidity and the effects of early stressors will require research incorporating biologic components, e.g., genetic variants and brain imaging. The lack of treatment for alcohol and drug disorders, predicted by attitudinal rather than financial variables, suggests urgent need for public and professional education to reduce the stigma associated with these disorders, and increase knowledge of treatment options.

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Section: Major Findingsmentioning

confidence: 99%

The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) Waves 1 and 2: review and summary of findings

Hasin

Grant

2015

Soc Psychiatry Psychiatr Epidemiol

386

293

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“…Based on such classifications, it is possible to isolate differences among subgroups of individuals with alcohol use disorder (AUD) in terms of drinking patterns, associated adverse consequences, development of AUD, and comorbid substance use disorders or psychiatric symptoms. Although some of the identified subgroups show substantially similar characteristics across different models, none of the previous classification attempts have yet been considered as generally adequate in research and clinical environments [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…By including non-treatment seeking individuals in classification models, they could more accurately represent the less severe forms of AUD [5,11]. Various studies which have used general population or community-based samples have identified severity-based subgroups of drinkers [4,10,[12][13][14][15]. Here, each of the latent classes demonstrated quantitatively different item endorsement profiles on the indicators of alcohol consumption, dependence symptoms, and negative social consequences.…”

Section: Introductionmentioning

confidence: 99%

An Empirically Based Typology of Alcohol Users in a Community Sample Using Latent Class Analysis

et al. 2019

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“…Recent population data indicate that Type B drinkers experience greater odds of persistent drinking problems and poorer response to treatment over three year follow-up (Tam, Mulia, & Schmidt, 2014). Investigations of alcohol typology derived strictly by diagnostic information yield inconsistent results suggesting as many as five groups varying in severity (e.g., Beseler et al, 2012; Casey, Adamson, & Stringer, 2013; Ko et al, 2012), although prospective studies suggest age of onset distinguishes these typologies with adult onset alcohol dependent drinkers experiencing more internalizing symptoms than early onset individuals (Meier et al, 2013). Questions about these strictly categorical models and their results led Hasin and colleagues (2013) to recommend that alcohol diagnosis constitute one category varying in dimensional severity for Diagnostic and Statistical Manual (DSM )-5.…”

Section: Introductionmentioning

confidence: 99%

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

Hildebrandt

Epstein

Sysko

et al. 2017

Alcoholism Clin & Exp Res

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Background The type A/B classification model for alcohol use disorders (AUDs) has received considerable empirical support. However, few studies examine the underlying latent structure of this subtyping model, which has been challenged as a dichotomization of a single drinking severity dimension. Type B, relative to type A, alcoholics represent those with early age of onset, greater familial risk, and worse outcomes from alcohol use. Method We examined the latent structure of the type A/B model using categorical, dimensional, and factor mixture models in a mixed gender community treatment-seeking sample of adults with an AUD. Results Factor analytic models identified 2-factors (drinking severity/externalizing psychopathology and internalizing psychopathology) underlying the type A/B indicators. A factor mixture model with 2-dimensions and 3-classes emerged as the best overall fitting model. The classes reflected a type A class and two type B classes (B1 and B2) that differed on the respective level of drinking severity/externalizing pathology and internalizing pathology. Type B1 had a greater prevalence of women and more internalizing pathology and B2 had a greater prevalence of men and more drinking severity/externalizing pathology. The 2-factor, 3-class model also exhibited predictive validity by explaining significant variance in 12-month drinking and drug use outcomes. Conclusions The model identified in the current study may provide a basis for examining different sources of heterogeneity in the course and outcome of AUDs.

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Empirical derived AUD sub types in the US general population: A latent class analysis

Cited by 18 publications

References 22 publications

The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) Waves 1 and 2: review and summary of findings

The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) Waves 1 and 2: review and summary of findings

An Empirically Based Typology of Alcohol Users in a Community Sample Using Latent Class Analysis

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

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