Junru Zhao1, Braden Linn2, Clara Bradizza1, Joseph Lucke3, Melanie Ruszczyk1, Paul Stasiewicz1. 1. School of Social Work, University at Buffalo-The State University of New York, 1021 Main Street, Buffalo, NY 14203, USA. 2. Clinical and Research Institute on Addictions, University at Buffalo-The State University of New York, 1021 Main Street, Buffalo, NY 14203, USA. 3. Department of Psychiatry, University at Buffalo-The State University of New York, 1021 Main Street, Buffalo, NY 14203, USA.
Abstract
AIMS: This study sought to identify phenotypic variations among individuals with alcohol use disorder (AUD) that may, in part, help improve the effectiveness of existing AUD interventions. METHODS: Latent class analysis was conducted to examine the potential heterogeneity of AUD in a sample (N = 220; Mage = 51.19 years, standard deviation = 9.94; 37.7% female) of treatment-seeking participants diagnosed with AUD using DSM-5 criteria. RESULTS AND CONCLUSIONS: Three distinct patterns of responses to the 11 DSM-5 AUD symptoms emerged: Class 1 (n = 114, 51.8%), Class 2 (n = 78, 35.5%) and Class 3 (n = 28, 12.7%). The identified profiles were further differentiated by demographics, alcohol-related constructs, individual difference characteristics and diagnostic and treatment variables. The findings have implications for refining AUD assessment as well as optimizing personalized treatment.
AIMS: This study sought to identify phenotypic variations among individuals with alcohol use disorder (AUD) that may, in part, help improve the effectiveness of existing AUD interventions. METHODS: Latent class analysis was conducted to examine the potential heterogeneity of AUD in a sample (N = 220; Mage = 51.19 years, standard deviation = 9.94; 37.7% female) of treatment-seeking participants diagnosed with AUD using DSM-5 criteria. RESULTS AND CONCLUSIONS: Three distinct patterns of responses to the 11 DSM-5 AUD symptoms emerged: Class 1 (n = 114, 51.8%), Class 2 (n = 78, 35.5%) and Class 3 (n = 28, 12.7%). The identified profiles were further differentiated by demographics, alcohol-related constructs, individual difference characteristics and diagnostic and treatment variables. The findings have implications for refining AUD assessment as well as optimizing personalized treatment.
Authors: João Mauricio Castaldelli-Maia; Camila M Silveira; Erica R Siu; Yuan-Pang Wang; Igor A Milhorança; Clóvis Alexandrino-Silva; Guilherme Borges; Maria C Viana; Arthur G Andrade; Laura H Andrade; Silvia S Martins Journal: Drug Alcohol Depend Date: 2014-01-03 Impact factor: 4.492