Intensity of Daily Drinking and Its Relation to Alcohol Use Disorders

Vergés, Álvaro; Ellingson, Jarrod M.; Schroder, Stephanie A.; Slutske, Wendy S.; Sher, Kenneth J.

doi:10.1111/acer.13812

Cited by 15 publications

(15 citation statements)

References 55 publications

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“…Relatedly, dissociation of tolerance from other aspects of dependence is well established in the tobacco literature (Perkins, 2002). Third, tolerance is not a strong marker of severity or dependence after controlling for heavy consumption (Martin et al, 2008; Vergés et al, 2018). Fourth and finally, tolerance does not substantially increment withdrawal in terms of defining physiological dependence (Schuckit et al, 1998).…”

Section: Discussionmentioning

confidence: 99%

Does crude measurement contribute to observed unidimensionality of psychological constructs? A demonstration with DSM–5 alcohol use disorder.

Watts¹,

Boness²,

Stevens³

et al. 2021

Journal of Abnormal Psychology

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Mental disorders are complex, multifaceted phenomena that are associated with profound heterogeneity and comorbidity. Despite the heterogeneity of mental disorders, most are generally considered unitary dimensions. We argue that certain measurement practices, especially using too few indicators per construct, preclude the detection of meaningful multidimensionality. We demonstrate the implications of crude measurement for detecting construct multidimensionality with alcohol use disorder (AUD). To do so, we used a large sample of college heavy drinkers (N = 909) for whom AUD symptomology was thoroughly assessed (87 items) and a blend of confirmatory factor analysis, exploratory factor analysis, and hierarchical clustering. A unidimensional AUD model with one item per symptom criterion fit the data well, whereas a unidimensional model with all items fit the data poorly. Starting with an 11-item AUD model, model fit decreased and the variability in factor loadings increased as additional items were added to the model. Additionally, multidimensional models outperformed unidimensional ones in terms of variance explained in theoretically relevant external criteria. All told, we converged on a hierarchically organized model of AUD with three broad, transcriterial dimensions that reflected tolerance, withdrawal, and loss of control. In addition to introducing a hierarchical model of AUD, we propose that thorough assessment of psychological constructs paired with serious consideration of alternative, multidimensional structures can move past the deadlock of their unidimensional representations. General Scientific SummaryWe show how crude measurement can essentially determine the inferences we draw about the dimensionality of diagnostic constructs. We use alcohol use disorder as an example of this general argument because it is a heterogeneous construct that is assumed to reflect a unitary dimension. With thorough assessment, we propose a hierarchically organized, multidimensional account of alcohol use disorder.

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Section: Discussionmentioning

confidence: 99%

Does crude measurement contribute to observed unidimensionality of psychological constructs? A demonstration with DSM–5 alcohol use disorder.

Watts¹,

Boness²,

Stevens³

et al. 2021

Journal of Abnormal Psychology

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Section: Moving Towards a Hierarchical Multidimensional Conceptualizmentioning

confidence: 97%

Does crude measurement contribute to observed unidimensionality of psychological constructs? An example with DSM-5 alcohol use disorder

Watts¹,

Boness²,

Stevens³

et al. 2020

Preprint

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Psychiatric diagnoses are complex, multifaceted phenomena that are associated with profound heterogeneity and comorbidity. Despite the heterogeneity of psychiatric diagnoses, most are generally considered unitary dimensions. We argue that certain measurement practices, especially using too few indicators per construct, preclude the detection of meaningful multidimensionality. We demonstrate the implications of crude measurement for detecting multidimensionality within constructs using alcohol use disorder (AUD) as an example. To do so, we used a large sample of college heavy drinkers (N=909) for whom AUD symptomology was thoroughly assessed (87 items) to examine multidimensionality in AUD using a blend of confirmatory factor analysis, exploratory factor analysis, and hierarchical clustering. A unidimensional model of AUD fit the data poorly and was outperformed by a multidimensional model in terms of variance explained in theoretically-relevant external criteria. We converged on a hierarchically-organized model of AUD with three broad, transcriterial dimensions that reflected Tolerance, Withdrawal, and Loss of Control. In addition to introducing a hierarchical model of AUD, we offer that thorough, improved assessment of psychiatric diagnoses paired with serious consideration of alternative, multidimensional structures can move past the deadlock of unidimensional representations of psychiatric disorders.

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“…The issue surrounding the diagnostic importance of tolerance is more complex. We recently reported (Vergés et al, 2018) that there was virtually no association between self-reported alcohol sensitivity and other symptoms of alcohol dependence after controlling for the heaviness of consumption patterns among daily drinkers. We speculate that tolerance is an intermediate phenotype that puts one at risk for addiction by virtue of the fact that it facilitates high-level exposures but is not part of addiction itself.…”

Section: )mentioning

confidence: 99%

The influence of sample selection on the structure of psychopathology symptom networks: An example with alcohol use disorder.

Hoffman¹,

Steinley²,

Trull³

et al. 2019

Journal of Abnormal Psychology

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Increasingly, the structure of mental disorders has been studied in the form of a network, characterizing how symptoms or criteria interact with and influence each other. Many studies of psychiatric symptoms and diagnostic criteria employ community or population-based surveys using co-occurrence of the symptoms/criteria to form the networks. However, given the overall low prevalence rates of mental disorders and their symptoms in the general population, most of those surveyed may not exhibit or endorse any symptoms and yet are often included in network analyses. Consequently, because network models are built on associations between symptoms/criteria, much of the observed variability is driven by individuals who are asymptomatic. Using data from the National Epidemiological Survey of Alcohol and Related Conditions (NESARC) Wave 2 and NESARC-III, we explore the effect of these “asymptomatic” observations on the estimated relations among diagnostic criteria of alcohol use disorder to determine the effects of such observations on estimated networks. We do so using the eLasso tool, as well as with traditional measures of correlation between binary variables (the Φ coefficient and odds ratio). We find that when the proportion of asymptomatic individuals are systematically culled from the sample, the estimated pairwise relations are often significantly affected, even changing signs in some cases. Our findings indicate that researchers should carefully consider the population(s) included in their sample and the implications it has on their interpretations of pairwise similarity estimates and resulting generalizability and reproducibility of estimates of network structures.

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Intensity of Daily Drinking and Its Relation to Alcohol Use Disorders

Cited by 15 publications

References 55 publications

Does crude measurement contribute to observed unidimensionality of psychological constructs? A demonstration with DSM–5 alcohol use disorder.

Does crude measurement contribute to observed unidimensionality of psychological constructs? A demonstration with DSM–5 alcohol use disorder.

Does crude measurement contribute to observed unidimensionality of psychological constructs? An example with DSM-5 alcohol use disorder

The influence of sample selection on the structure of psychopathology symptom networks: An example with alcohol use disorder.

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