Modeling alcohol use disorder as a set of interconnected symptoms – Assessing differences between clinical and population samples and across external factors

K. B. S. Huth, J. Luigjes, M. Marsman, A. E. Goudriaan, R. J. van Holst

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Alcohol use disorder is argued to be a highly complex disorder influenced by a multitude of factors on different levels. Common research approaches fail to capture this breadth of interconnecting symptoms. To address this gap in theoretical assumptions and methodological approaches, we used a network analysis to assess the interplay of alcohol use disorder symptoms. We applied the exploratory analysis to two US-datasets, a population sample with 23,591 individuals and a clinical sample with 483 individuals seeking treatment for alcohol use disorder. Using a Bayesian framework, we first investigated differences between the clinical and population sample looking at the symptom interactions and underlying structure space. In the population sample the time spent drinking alcohol was most strongly connected, whereas in the clinical sample loss of control showed most connections. Furthermore, the clinical sample demonstrated less connections, however, estimates were too unstable to conclude the sparsity of the network. Second, for the population sample we assessed whether the network was measurement invariant across external factors like age, gender, ethnicity and income. The network differed across all factors, especially for age subgroups, indicating that subgroup specific networks should be considered when deriving implications for theory building or intervention planning. Our findings corroborate known theories of alcohol use disorder stating loss of control as a central symptom in alcohol dependent individuals.
Original languageEnglish
Article number107128
JournalAddictive Behaviors
Volume125
DOIs
Publication statusPublished - 1 Feb 2022

Keywords

  • Alcohol use disorder
  • Bayesian analysis
  • Clinical and population sample
  • Loss of control
  • Measurement invariance
  • Network analysis

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