Home > Problem and non-problem gamblers: a cross-sectional clustering study by gambling characteristics.

Guillou Landreat, Morgane and Chereau Boudet, Isabelle and Perrot, Bastien and Romo, Lucia and Codina, Irene and Magalon, David and Fatseas, Melina and Luquiens, Amandine and Brousse, Georges and Challet-Bouju, Gaëlle and Grall-Bronnec, Marie (2020) Problem and non-problem gamblers: a cross-sectional clustering study by gambling characteristics. BMJ Open, 10, (e030424), e030424. doi: 10.1136/bmjopen-2019-030424.

External website: https://bmjopen.bmj.com/content/10/2/e030424

OBJECTIVES: Gambling characteristics are factors that could influence problem gambling development. The aim of this study was to identify a typology of gamblers to frame risky behaviour based on gambling characteristics (age of initiation/of problem gambling, type of gambling: pure chance/chance with pseudoskills/chance with elements of skill, gambling online/offline, amount wagered monthly) and to investigate clinical factors associated with these different profiles in a large representative sample of gamblers.

DESIGN AND SETTING: The study is a cross-sectional analysis to the baseline data of the french JEU cohort study (study protocol : Challet-Bouju , 2014). Recruitment (April 2009 to September 2011) involved clinicians and researchers from seven institutions that offer care for or conduct research on problem gamblers (PG). Participants were recruited in gambling places, and in care centres. Only participants who reported gambling in the previous year between 18 and 65 years old were included.Participants gave their written informed consent, it was approved by the French Research Ethics Committee.

PARTICIPANTS: The participants were 628 gamblers : 256 non-problem gamblers (NPG), 169 problem gamblers without treatment (PGWT) and 203 problem gamblers seeking treatment (PGST).

RESULTS: Six clustering models were tested, the one with three clusters displayed a lower classification error rate (7.92%) and was better suited to clinical interpretation : 'Early Onset and Short Course' (47.5%), 'Early Onset and Long Course' (35%) and 'Late Onset and Short Course' (17.5%). Gambling characteristics differed significantly between the three clusters.

CONCLUSIONS: We defined clusters through the analysis of gambling variables, easy to identify, by psychiatrists or by physicians in primary care. Simple screening concerning these gambling characteristics could be constructed to prevent and to help PG identification. It is important to consider gambling characteristics : policy measures targeting gambling characteristics may reduce the risk of PG or minimise harm from gambling.


Item Type
Article
Publication Type
International, Open Access, Article
Drug Type
Behavioural addiction
Source
Date
18 February 2020
Identification #
doi: 10.1136/bmjopen-2019-030424
Page Range
e030424
Volume
10
Number
e030424
EndNote

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