Susana Jiménez-Murcia1,2,3,4, Roser Granero5,6, Mònica Giménez7,8, Amparo Del Pino-Gutiérrez7,9, Gemma Mestre-Bach5,7, Teresa Mena-Moreno5,7, Laura Moragas7, Marta Baño7, Jéssica Sánchez-González7, Marta de Gracia7, Isabel Baenas-Soto7, S Fabrizio Contaldo7, Eduardo Valenciano-Mendoza7,8, Bernat Mora-Maltas7, Hibai López-González7, José M Menchón7,10,8, Fernando Fernández-Aranda5,7,10,11. 1. Ciber Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto Salud Carlos III, Madrid, Spain. sjimenez@bellvitgehospital.cat. 2. Department of Psychiatry, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain. sjimenez@bellvitgehospital.cat. 3. Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Spain. sjimenez@bellvitgehospital.cat. 4. Department of Clinical Sciences, School of Medicine, Universitat de Barcelona-UB, L'Hospitalet de Llobregat, Spain. sjimenez@bellvitgehospital.cat. 5. Ciber Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto Salud Carlos III, Madrid, Spain. 6. Department of Psychobiology and Methodology, Autonomous University of Barcelona, Barcelona, Spain. 7. Department of Psychiatry, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain. 8. CIBER Salud Mental (CIBERSam), Instituto de Salud Carlos III, Madrid, Spain. 9. Department of Public Health, Mental Health and Perinatal Nursing, School of Nursing, University of Barcelona-UB, Barcelona, Spain. 10. Psychiatry and Mental Health Group, Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Spain. 11. Department of Clinical Sciences, School of Medicine, Universitat de Barcelona-UB, L'Hospitalet de Llobregat, Spain.
Abstract
BACKGROUND: There are no studies based on a person-centered approach addressing sex-related differences in the characteristics of treatment-seeking patients with gambling disorder (GD). The main objective of the current study is to identify empirical clusters of GD based on several measures of the severity of gambling behavior, and considering the potential role of patient sex as a moderator. METHODS: An agglomerative hierarchical clustering method was applied to an adult sample of 512 treatment-seeking patients (473 men and 39 women) by using a combination of the Schwarz Bayesian Information Criterion and log-likelihood function. RESULTS: Three clusters were identified in the subsample of men: cluster M1 (low-mild gambling severity level, 9.1%), cluster M2 (moderate level, 60.9%), and cluster M3 (severe level, 30.0%). In the women subsample, two clusters emerged: cluster W1 (mild-moderate level, 64.1%), and cluster W2 (severe level, 35.9%). The most severe GD profiles were related to being single, multiple gambling preference for nonstrategic plus strategic games, early onset of the gambling activity, higher impulsivity levels, higher dysfunctional scores in the personality traits of harm avoidance, and self-directedness, and higher number of lifespan stressful life events (SLE). Differences between the empirical men and women clusters were found in different sociodemographic and clinical measurements. CONCLUSIONS: Men and women have distinct profiles regarding gambling severity that can be identified by a clustering approach. The sociodemographic and clinical characterization of each cluster by sex may help to establish specific preventive and treatment interventions.
BACKGROUND: There are no studies based on a person-centered approach addressing sex-related differences in the characteristics of treatment-seeking patients with gambling disorder (GD). The main objective of the current study is to identify empirical clusters of GD based on several measures of the severity of gambling behavior, and considering the potential role of patient sex as a moderator. METHODS: An agglomerative hierarchical clustering method was applied to an adult sample of 512 treatment-seeking patients (473 men and 39 women) by using a combination of the Schwarz Bayesian Information Criterion and log-likelihood function. RESULTS: Three clusters were identified in the subsample of men: cluster M1 (low-mild gambling severity level, 9.1%), cluster M2 (moderate level, 60.9%), and cluster M3 (severe level, 30.0%). In the women subsample, two clusters emerged: cluster W1 (mild-moderate level, 64.1%), and cluster W2 (severe level, 35.9%). The most severe GD profiles were related to being single, multiple gambling preference for nonstrategic plus strategic games, early onset of the gambling activity, higher impulsivity levels, higher dysfunctional scores in the personality traits of harm avoidance, and self-directedness, and higher number of lifespan stressful life events (SLE). Differences between the empirical men and women clusters were found in different sociodemographic and clinical measurements. CONCLUSIONS: Men and women have distinct profiles regarding gambling severity that can be identified by a clustering approach. The sociodemographic and clinical characterization of each cluster by sex may help to establish specific preventive and treatment interventions.
Entities:
Keywords:
Clustering; Gambling disorder; Personality; Psychopathology; Sex
Authors: Tara Elton-Marshall; Rochelle Wijesingha; Taryn Sendzik; Steven E Mock; Mark van der Maas; John McCready; Robert E Mann; Nigel E Turner Journal: Can J Aging Date: 2018-07-13
Authors: John B Saunders; Wei Hao; Jiang Long; Daniel L King; Karl Mann; Mira Fauth-Bühler; Hans-Jürgen Rumpf; Henrietta Bowden-Jones; Afarin Rahimi-Movaghar; Thomas Chung; Elda Chan; Norharlina Bahar; Sophia Achab; Hae Kook Lee; Marc Potenza; Nancy Petry; Daniel Spritzer; Atul Ambekar; Jeffrey Derevensky; Mark D Griffiths; Halley M Pontes; Daria Kuss; Susumu Higuchi; Satoko Mihara; Sawitri Assangangkornchai; Manoj Sharma; Ahmad El Kashef; Patrick Ip; Michael Farrell; Emanuele Scafato; Natacha Carragher; Vladimir Poznyak Journal: J Behav Addict Date: 2017-08-17 Impact factor: 6.756