Clustering of treatment-seeking women with gambling disorder

J Behav Addict. 2018 Sep 1;7(3):770-780. doi: 10.1556/2006.7.2018.93. Epub 2018 Sep 21.

Abstract

Background: The prevalence of gambling disorder (GD) in women has increased, but, to date, few studies have explored the features of clinical GD subtypes in female samples.

Aims: The aim of this study is to identify empirical clusters based on clinical/sociodemographic variables in a sample of treatment-seeking women with GD.

Methods: Agglomerative hierarchical clustering was applied to a sample of n = 280 patients, using sociodemographic variables, psychopathology, and personality traits as indicators for the grouping procedure.

Results: Three mutually exclusive groups were obtained: (a) Cluster 1 (highly dysfunctional; n = 82, 29.3%) endorsed the highest levels in gambling severity, comorbid psychopathology, novelty seeking, harm avoidance, and self-transcendence, and the lowest scores in self-directedness and cooperativeness; (b) Cluster 2 (dysfunctional; n = 142, 50.7%) achieved medium mean scores in gambling severity and psychopathological symptoms; and (c) Cluster 3 (functional; n = 56, 20.0%) obtained the lowest mean scores in gambling severity and in psychopathology, and a personality profile characterized by low levels in novelty seeking, harm avoidance, and self-transcendence, and the highest levels in self-directedness and cooperativeness.

Discussion and conclusions: This study sheds light on the clinical heterogeneity of women suffering from GD. Identifying the differing features of women with GD is vital to developing prevention programs and personalized treatment protocols for this overlooked population.

Keywords: assessment; gambling disorder; personality traits; psychopathology; women.

MeSH terms

  • Cluster Analysis
  • Female
  • Gambling / epidemiology*
  • Gambling / psychology
  • Gambling / therapy
  • Humans
  • Middle Aged
  • Patient Acceptance of Health Care
  • Personality
  • Socioeconomic Factors

Grants and funding

Funding sources: Financial support was received through the Ministerio de Economía y Competitividad (grant PSI2011-28349 and PSI2015-68701-R). FIS PI14/00290, FIS PI17/01167, and 18MSP001 – 2017I067 received aid from the Ministerio de Sanidad, Servicios Sociales e Igualdad. CIBER Fisiología Obesidad y Nutrición (CIBERobn) and CIBER Salud Mental (CIBERSAM), both of which are initiatives of ISCIII. GM-B is supported by a predoctoral Grant of AGAUR (2018 FI_B2 00174), co-financed by the European Social Fund, with the support of the Secretaria d’Universitats I Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya. TM-M is supported by a predoctoral Grant of the Ministerio de Educación, Cultura y Deporte (FPU16/02087).