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  • 1 BAS Bayerische Akademie für Sucht- und Gesundheitsfragen (Unternehmergesellschaft haftungsbeschränkt), LSG Landesstelle Glücksspielsucht in Bayern, Munich, Germany
  • | 2 IFT Institut für Therapieforschung, Munich, Germany
Open access

Abstract

Background and aim

A wide range of studies indicates that men and women with Problem (PrG) and Pathological Gambling (PG) differ in several clinical and sociodemographic characteristics. However, evidence for sex differences, such as the telescoping effect, is contradictory, and it is still unclear whether sex differences observed in offline gambling can also be found for online gambling. Furthermore, reviews have so far focused on binary sex differences but neglect gender aspects. In this study, an updated literature survey of sex- and gender-related differences in PrG and PG was conducted.

Methods

We searched PsyInfo, Medline/Pubmed, and the Web of Science databases from 2005 to 2020 for studies investigating sex and gender differences in gambling. A total of 126 papers were included in the literature survey.

Results

We are presenting our findings according to the categories ‘prevalence’ (offline, online, LGBTQI*), ‘sociodemographic factors’, ‘preferred gambling type’, ‘gambling motives’, ‘severity’, ‘progression of gambling problems’, ‘use of professional help/motivation for treatment’, ‘comorbidity’, ‘trauma’, ‘violence and criminality/delinquency’. The studies indicate that, despite some robust sex differences (e.g., concerning prevalence rates), results for most areas were mixed or suggest no sex differences (e.g., violence, gambling motives).

Discussion and conclusion

To date, there is a lack of studies assessing gender, and not only sex, warranting further research in this area.

Abstract

Background and aim

A wide range of studies indicates that men and women with Problem (PrG) and Pathological Gambling (PG) differ in several clinical and sociodemographic characteristics. However, evidence for sex differences, such as the telescoping effect, is contradictory, and it is still unclear whether sex differences observed in offline gambling can also be found for online gambling. Furthermore, reviews have so far focused on binary sex differences but neglect gender aspects. In this study, an updated literature survey of sex- and gender-related differences in PrG and PG was conducted.

Methods

We searched PsyInfo, Medline/Pubmed, and the Web of Science databases from 2005 to 2020 for studies investigating sex and gender differences in gambling. A total of 126 papers were included in the literature survey.

Results

We are presenting our findings according to the categories ‘prevalence’ (offline, online, LGBTQI*), ‘sociodemographic factors’, ‘preferred gambling type’, ‘gambling motives’, ‘severity’, ‘progression of gambling problems’, ‘use of professional help/motivation for treatment’, ‘comorbidity’, ‘trauma’, ‘violence and criminality/delinquency’. The studies indicate that, despite some robust sex differences (e.g., concerning prevalence rates), results for most areas were mixed or suggest no sex differences (e.g., violence, gambling motives).

Discussion and conclusion

To date, there is a lack of studies assessing gender, and not only sex, warranting further research in this area.

Introduction

Problem gambling (PrG) and pathological gambling (PG) are well recognized societal and individual health issues (Marshall, 2009; The Lancet, 2017). PrG and PG are characterized by a strong urge to gamble with increasing amounts of money and loss of control. The gambling activities go along with significant financial losses and legal problems. Frequently, persons with PrG and PG risk their relationships and jobs and report severe psychosocial problems (American Psychiatric Association, 2013).

Aside from these general diagnostic criteria, the clinical picture may vary considerably by sex and gender. Whereas sex refers to the biological characteristics, gender focuses on societal and cultural norms, preferences as well as behaviors (WHO, 2019).

Regarding sex differences, some studies report that men are twice as likely to develop a gambling problem as women (Banz & Lang, 2017) and that men prefer skill-based games such as poker, whereas women prefer strictly chance-based games such as slot-machines or bingo (Bonnaire et al., 2017; Odlaug, Marsh, Kim, & Grant, 2011; Potenza, Maciejewski, & Mazure, 2006). Another frequently mentioned example is the telescoping effect (e.g., Blanco, Hasin, Petry, Stinson, & Grant, 2006; Grant, Odlaug, & Mooney, 2012; Tavares et al., 2003), suggesting that women start to gamble later in life but progress to pathological gambling behavior faster. It has also been hypothesized that women gamble for different motives (Echeburúa et al., 2011, 2013; Grant & Kim, 2002). Whereas men gamble to earn money easily or to seek excitement, women mainly want to escape boredom, loneliness and everyday problems. Men enter gambling-related treatment at a younger age than women, show more difficulties concerning their alcohol use and are more often in conflict with the law (Ladd & Petry, 2002; Potenza et al., 2001). Men and women also seem to differ in comorbidities. Some studies reported a higher probability of comorbid disorders in women (Blanco et al., 2006; Desai & Potenza, 2008; Díez, Aragay, Soms, Prat, & Casas, 2014). Elevated comorbidity rates have been found most often for affective and anxiety disorders. However, despite the wide variety of areas where sex differences between men and women have been observed in their gambling behavior and related problems, evidence so far has been mixed. Even for the often-cited telescoping effect, findings are contradictory (Slutske, Piasecki, Deutsch, Statham, & Martin, 2014). An additional issue is that existing reviews about gambling related problems have focused on binary sex differences between women and men and lack a gender dimension (e.g., McCarthy, Thomas, Bellringer, & Cassidy, 2019; Merkouris et al., 2016). As gambling is a phenomenon shaped by sociocultural factors (e.g., How socially accepted is it for different genders to gamble?), gender aspects should be considered. Despite some attempts to explore gender differences in gambling, a common problem is that even though only sex is being assessed, gender differences are implied. To avoid a confusion of concepts, Clayton and Tannenbaum (2016) suggest strictly differentiating between the two concepts. Due to the importance of the two concepts are more detailed definition will be given in the following.

Sex can be categorized as male, female or intersex and is indicated for example by one's sex chromosomes or reproductive organs. Whereas sex is a construct focusing on medical assumptions, gender highlights cultural and social aspects. Gender identity can be defined as a person's inner sense of being female, male or something else. It does not necessarily have to be congruent with a person's sex assigned at birth nor is it always visible to others. Gender includes the concept gender identity as well as gender expression. The external appearance of an individual (e.g., clothing, behavior, voice) is defined as gender expression and can be contrary to one's gender identity. In short gender identity is best captured as a continuum ranging from woman-ness to man-ness whereas gender-expression could be measured on a scale from femininity to masculinity. Another social construct, that is closely linked to gender, is sexual orientation, which refers to the sexual and emotional attraction to another person.

As the definitions illustrate, sex and gender comprise two distinct concepts. Even though they might influence or complement each other, sex and gender focus on different aspects. Therefore, it is necessary to examine them separately. Differentiating between sex and gender aspects is not only of scientific interest. For example, it may help clarify reasons for differences in prevalence rates and treatment seeking behavior.

The aim of this study is to critically re-examine assumed sex and gender differences based on accumulated empirical evidence. A special focus is on identifying studies explicitly including gender aspects. The summary is intended to inform good clinical practice by providing a guideline about which differences between male, female, and gender diverse persons with PrG and PG should be considered in treatment.

Methods

Selection criteria

To be included in the present narrative review, studies had to be peer-reviewed and available to the authors no later than November 2020. Only studies on predefined areas of interest were included. Overall, we defined 11 areas of interest: (1) prevalence rates (offline, online, LGBTGI* [Lesbian, Gay, Bisexual, Transgender, queer, intersex and others]); (2) sociodemographic factors (educational level, marital status, income, debts, children); (3) preferred gambling types; (4) gambling motives; (5) severity of gambling problems; (6) progression of gambling problems (age/telescoping effect); (7) use of help/motivation for treatment; (8) comorbidity; (9) trauma; (10) violence; (11) criminality and delinquency.

Procedure for identification of studies

We searched PsyInfo, Medline/Pubmed, and the Web of Science databases from 2005 to 2020 for clinical studies investigating sex and gender differences in gambling. We used the search term “(problem OR addict* OR pathological OR risk OR disorder) AND gambling AND (gender OR sex OR male OR female OR men OR women OR man OR woman)”. To search for specific sub-categories, further search terms were added (AND “prevalence”, “comorbidity” …). All peer-reviewed papers published in German or English between 2005 and 2020 were scanned based on the title and information given in the abstract and keywords (full text available, specifically addressed gender differences). Additionally, we manually scanned the reference lists of relevant papers and included papers that were considered suitable. Pivotal gray literature (e.g., addiction surveys) was included as well. To be included in the review, studies had to have at least some focus on sex or gender differences. Studies in which this aspect was considered rather superficially, e.g., as a covariate in the analyses and only discussed in passing, were not included.

Results

Overall, we identified 1970 articles. After removing duplicates and scanning the abstracts, 126 articles were included. As some articles contained relevant information on more than one of the predefined areas, they will be cited separately in each category. 126 of the included articles examined sex. None provided information how the construct gender was assessed even if the title or abstract implied it. Therefore, we can't present any findings regarding gender differences in the results section.

Prevalence

Offline. Noting that partly different instruments were used to assess gambling-related problems among the different studies, the 26 included studies indicate a prevalence spectrum from 0.3% to 10.9% for at least problematic gambling (Abbott, Stone, Billi, & Yeung, 2016; Anagnostopoulos et al., 2017; Andrie et al., 2019; Assanangkornchai, McNeil, Tantirangsee, & Kittirattanapaiboon, 2016; Baggio et al., 2018; Castrén, Heiskanen, & Salonen, 2018; Blanco et al., 2006; Brodbeck, Duerrenberger, & Znoj, 2009; Castrén et al., 2013; Chiu & Woo, 2012; Economou et al., 2019; Fröberg et al., 2015; Giralt et al., 2018; Gori et al., 2015; Huang & Boyer, 2007; Lupu & Todirita, 2013; Melendez-Torres, Anthony, Hewitt, Murphy, & Moore, 2020; Mori & Goto, 2020; Nordmyr, Forsman, Wahlbeck, Björkqvist, & Österman, 2014; Petry & Steinberg, 2005; Petry, Stinson, & Grant, 2005; Svensson & Romild, 2014; van der Maas et al., 2018; Welte, Barnes, Tidwell, Hoffman, & Wieczorek, 2015; Williams, Lee, & Back, 2013; Wu, Lai, & Tong, 2014). Within this spectrum, men were usually more often affected by problematic and pathological gambling behavior than women. This general trend could be observed for adolescents and adults (Assanangkornchai et al., 2016; Blanco et al., 2006; Brodbeck et al., 2009; Castrén et al., 2013; Gori et al., 2015; Huang & Boyer, 2007; Mori & Goto, 2020). However, in one Swedish study (Svensson & Romild, 2014), there was a deviation from this pattern as females showed a higher prevalence after controlling for age and gambling type domains.

The extent of observed differences varied within different age groups. In studies addressing adolescent gambling, men exhibited higher ratios for PrG and PG compared with women (Anagnostopoulos et al., 2017; Andrie et al., 2019; Chiu & Woo, 2012; Gori et al., 2015; Huang & Boyer, 2007; Lupu & Todirita, 2013). According to a study by Blanco et al. (2006), men generally appeared to be affected by gambling problems at a younger age, whereas women appeared to be affected more often at an older age. In a study among the over 55-year-olds, differences could only be found with regard to general aspects of gambling (e.g., frequency, attitudes) and not with regard to the prevalence of PrG (van der Maas et al., 2018). The longitudinal comparison of two US surveys conducted in 1999–2000 and 2011–2013 found increasing PrG rates for men and decreasing rates for women (Welte et al., 2015), whereas a Finnish study by Castrén, Heiskanen, and Salonen (2018) observed the opposite with a decrease in men's PrG from 2007 to 2011, whereas women's at-risk gambling and problem gambling increased from 2011 to 2015 (see Table 1 at section ‘prevalence of offline gambling’).

Table 1.

126 papers for the literature survey

StudyCountrySample SizeSex (%)Mean age (SD, age range)Sample typeProblem gambling measures
Prevalence of offline gambling
Abbott et al. (2016)Australia23,479Male: n = 9,473

Female: n = 14,006
AdultsGeneral populationSOGS, CPGI, VGS
Anagnostopoulos et al. (2017)Greece2,141Male: 45.4%

Female: 54.6%
AdolescentsGeneral populationDSM-IV
Andrie et al. (2019)Spain, Greece, The Netherlands, Poland, Romania, Germany, Iceland13,284Male: n = 6,284

Female: n = 7,000
AdolescentsGeneral populationSOGS
Assanangkornchai et al. (2016)Thailand4,727Male: 36%

Female: 64%
AdultsGeneral populationDSM-IV
Baggio et al. (2018)France8,805Male: 48.2%

Female: 51.8%
Adolescents, Adults, Older AdultsGeneral populationPGSI
Blanco et al. (2006)USA43,093Only reported for participants with GDAdultsGeneral populationAUDADIS-IV
Brodbeck et al. (2009)Switzerland8,385Male: 48%

Female: 52%
AdultsGeneral populationNODS
Castrén et al. (2013)Finland2,826Male: n = 1,243

Female: n = 1,583
Adolescents, AdultsGeneral populationPGSI
Castrén, Heiskanen, and Salonen (2018)Finland13,721Male: n = 6,785

Female: n = 6,936
Adolescents, Adults, Older AdultsGeneral populationSOGS
Chiu and Woo (2012)Chinese in USA192Male: n = 97

Female: n = 93
AdolescentsGeneral populationSOGS
Economou et al. (2019)Greece7,818Male NPG: 46.7%

Male PG: 67.1%
AdultsGeneral and clinical samplePGSI
Fröberg et al. (2015)Sweden4,358Male: n = 2,217

Female: n = 2,141
Adolescents, AdultsGeneral populationPGSI, SOGS
Giralt et al. (2018)Germany9,309Male: n = 4,600

Female: n = 4,709
AdolescentsGeneral populationDSM-IV
Gori et al. (2015)Italy5,920Male: 62.3%

Female: 37.7%
AdolescentsGeneral populationSOGS
Huang and Boyer (2007)Canada5,666N/AAdolescents, Young AdultsGeneral populationCPGI
Lupu and Todirita (2013)Romania1,032Male: 65.57%

Female: 34.43%
AdolescentsGeneral population20-GA
Melendez-Torres et al. (2020)Wales37,363Male: n = 18,663

Female: n = 17,959
AdolescentsGeneral populationN/A
Mori and Goto (2020)Japan6,576Male: n = 3,302

Female: n = 3,274
AdultsGeneral populationSOGS
Nordmyr et al. (2014)Finland2,984Male: n = 1,438

Female: n = 1,546
AdultsGeneral populationDSM-IV
Petry and Steinberg (2005)USA149Male: n = 72

Female: n = 77
AdultsClinical sample (treatment seeking PGs)SOGS
Petry et al. (2005)USA42,898Male: 47.8%

Female: 52.3%
AdultsGeneral populationAUDADIS-IV
Svensson and Romild (2014)Sweden3,191Male: n = 2048

Female: n = 1,143
Adolescents, Adults, Older AdultsGeneral populationPGSI
van der Maas et al. (2018)Canada2,187Male: n = 1,011

Female: n = 1,176
Older AdultsGeneral populationCPGI
Welte et al. (2015)USA5,594Male: n = 2,703

Female: n = 2,891
AdultsGeneral populationSOGS
Williams et al. (2013)South Korea8,330N/AAdultsGeneral populationCPGI, NODS, PPGM
Wu et al. (2014)Macao1,018Male: n = 454

Female: n = 564
AdultsGeneral populationDSM-IV
Prevalence of online gambling
Chóliz et al. (2019)Spain6,816Male: 48.4%

Female: 51.6%
Adults, Older AdultsGeneral populationNODS
Edgren et al. (2017)Finland3,555Male: 53.8%

Female: 46.2%
Adults, Older AdultsGeneral populationPGSI
Elton-Marshall et al. (2016)Canada10,035Male: n = 4,937

Female: n = 5,098
AdolescentsGeneral populationGPSS of the CAGI
Floros et al. (2013)Island of Kos2017Male: 51.8%

Female: 48.2%
AdolescentsGeneral populationDSM-IV
Gainsbury et al. (2015)Australia15,006Male: 47.5%

Female: 52.5%
Adults, Older AdultsGeneral populationPGSI
Gómez et al. (2020)Spain3,772Male: 49.8%

Female: 50.2%
AdolescentsGeneral populationN/A
Griffiths et al. (2009)GB9,003N/AAdolescents, AdultsGeneral populationDSM-IV
Lelonek-Kuleta et al. (2020)Poland2000Male: 48.2%

Female: 51.8%
AdultsGeneral populationBBGS
McCormack et al. (2014)UK975Male: n = 800

Female: n = 175
AdultsGeneral populationPGSI
Wu et al. (2015)China952 community sample (CS)

427 university student sample (US)

(N = 1,399)
CS: Male: 42%, Female: 58%

US: Male: 43.6%, Female: 56.4%
CS: Adults

US: Young Adults
General populationDSM-IV
Yazdi and Katzian (2017)Austria3,043Only reported for participants with GDOnly reported for participants with GDGeneral populationJohnson's Lie-and-Bet Questionnaire (Johnson et al., 1997)
Prevalence of LGBTQI*
Broman and Hakansson (2018)Italy605Male: 38%

Female: 51%

Non-sexual: 11%
Adolescents, AdultsGeneral populationNODS, GAS
Grant and Potenza (2006)USA105Only maleAdultsClinical sampleDSM-IV
Richard et al. (2019)Canada19,299Male:

Hetero: n = 10,305

Gay: n = 137

Bi: n = 65

Female:

Hetero: n = 8,215

Gay: n = 274

Bi: n = 303
Young AdultsGeneral populationDSM-5
Sociodemographic factors
Blanco et al. (2006)USA43,093Only reported for participants with GDAdultsGeneral populationAUDADIS-IV
Bonnaire et al. (2016)France25,646Male: n = 12,504

Female: n = 13,142
Adolescents, Adults, Older AdultsGeneral populationCPGI
Bonnaire et al. (2017)France25,646Male: n = 12,504

Female: n = 13,142
Adolescents, Adults, Older AdultsGeneral populationCPGI
Castrén, Kontto, et al. (2018)Finland3,251Male: n = 1833

Female: n = 1,418
Adolescents, Adults, Older AdultsGeneral populationSOGS
Granero et al. (2009)Spain286Male: 50%

Female: 50%
AdultsClinical sampleSOGS, DSM-IV
Grant, Chamberlain, et al. (2012)USA501Male: n = 227

Female: n = 274
AdultsClinical sampleSCI-PG, CGI, PG-YBOGS, GSAS
Guillo-Landreat et al. (2016)France194Male: 82.47%

Female: 17.53%
AdultsClinical sampleDSM-IV, GRCS
Hing et al. (2016)Australia8,917Male: n = 3,783

Female: n = 3,903
AdultsGeneral populationPGSI
Jiménez-Murcia et al. (2020)Spain512Male: n = 473

Female: n = 39
AdultsClinical sampleDSM, GRSC
Ronzitti et al. (2016)UK1,178Male: 92.5%

Female: 7.5%
AdultsClinical samplePGSI
Vogelgesang (2009)Germany200Male: 50%

Female: 50%
AdultsClinical sampleICD-10
Preferred gambling type
Bonnaire et al. (2017)France25,647Male: n = 12,504

Female: n = 13,142
Adolescents, Adults, Older AdultsGeneral populationCPGI
Leung and Tsang (2011)Chinese in Hong Kong4,480Male: n = 3,949

Female: n = 531
AdultsClinical sampleN/A
Lopez-Gonzalez et al. (2020)Australia/Spain1,092Australian Male: 79.3%, Female: 20.7%

Spain Male: 72.0%, Female: 28.0%
AdultsGeneral populationPGSI
Nong et al. (2020)China855Male: 48.1%

Female: 51.9%
Adults, Older Adultslatent class analysisDSM-5
Nower and Blaszczynski (2006)USA2,670Male: 51.1%

Female: 48.4%
Adults, Older AdultsClinical sampleN/A
Odlaug et al. (2011)N/A440Male: 54.9%

Female: 45.1%
AdultsClinical samplePG-YBOGS, CGI
Potenza et al. (2006)US2,417Male: n = 1,131

Female: n = 1,231
AdultsGeneral populationNODS
Ronzitti et al. (2016)UK1,178Male: 92.5%

Female: 7.5%
AdultsClinical samplePGSI
Stark et al. (2012)Canada3,604N/AAdultsGeneral populationPGSI
Stevens and Young (2010)Australia1,172 people weighted to 97,525 to represent the resident Northern Territory adult population who had gambled in the previous 12 monthsMale: 52.4%

Female: 47.6%
AdultsGeneral populationN/A
Svensson and Romild (2014)Sweden3,191Male: n = 2048

Female: n = 1,143
Adolescents, Adults, Older AdultsGeneral populationPGSI
Toneatto and Wang (2009)Canada60Male: n = 44

Female: n = 16
AdultsClinical sampleCPGI
van der Maas et al. (2018)Canada2,187Male: n = 1,011

Female: n = 1,176
Older AdultsGeneral populationCPGI
Williams et al. (2013)South Korea8,330N/AAdultsGeneral populationCPGI, NODS, PPGM
Gambling motives
Clarke and Clarkson (2008)New Zealand104Male: n = 41

Female: n = 63
Older AdultsGeneral populationN/A
Clarke et al. (2007)New Zealand209Male: n = 61

Female: n = 148
Younger Adults, Older AdultsGeneral populationDSM-IV
Echeburúa et al. (2011)Spain103Male: n = 52

Female: n = 51
AdultsClinical sampleDSM-IV
Flack and Stevens (2018)Australia4,945N/AAdultsGeneral populationPGSI
Grant and Kim (2002)N/A131Male: 40%

Female: 60%
Adults, Older AdultsClinical sample (treatment seeking PGs)SOGS, GAF, CGI, DSM-IV
Hing et al. (2016)Australia8,917Male: n = 3,783

Female: n = 3,903
AdultsGeneral populationPGSI
McCormack et al. (2014)UK975Male: n = 800

Female: n = 175
AdultsGeneral populationPGSI
Sundqvist et al. (2016)Sweden257Male: n = 179

Female: n = 78
AdultsGeneral populationNODS-PERC, Johnson 's Lie-and-Bet Questionnaire (Johnson et al., 1997)
Walker et al. (2005)Canada400N/AAdultsGeneral populationN/A
Severity
Granero et al. (2009)Spain286Male: 50%

Female: 50%
AdultsClinical sampleSOGS, DSM-IV
Grant et al. (2017)N/A574Male: 54%

Female: 46%
AdultsClinical sampleCGI, SCI-GD, PG-YBOGS, GSAS
Grant, Chamberlain, et al. (2012)USA501Male: n = 227

Female: n = 274
AdultsClinical sampleSCI-PG, CGI, PG-YBOGS, GSAS
Håkansson and Widinghoff (2020)Sweden327Male: n = 223

Female: n = 104
AdultsClinical samplePGSI
Jiménez-Murcia et al. (2016)Spain1,632Male: 91.5%

Female: 8.5%
AdultsClinical sampleSOGS
Jiménez-Murcia et al. (2020)Spain512Male: n = 473

Female: n = 39
AdultsClinical sampleDSM, GRSC
Kim et al. (2016)New Zealand150Male: n = 64

Female: n = 86
AdultsClinical samplePGSI
Ronzitti et al. (2016)UK1,178Male: 92.5%

Female: 7.5%
AdultsClinical samplePGSI
Progression of gambling problems
Carneiro et al. (2014)Brazil118Only reported for subgroupsAdolescents, AdultsClinical sampleNODS, DSM-IV
Edgerton et al. (2015)Canada679Male: 48.2%

Female: 51.8%
Young adultsGeneral populationPGSI
Grant, Odlaug, and Mooney (2012)N/A71Male: 52.1%

Female: 47.9%
AdultsClinical sampleSCI-PG
Haw and Holdsworth (2016)Australia267Male: 54%

Female: 46%
AdultsClinical samplePGSI
Nelson et al. (2006)USA2,256Male: n = 1,239–1,258

Female: n = 921–935
AdultsClinical sampleDSM-IV
Slutske et al. (2014)Australia4,663Male: n = 2001

Female: n = 2,662
AdultsGeneral populationDSM-IV
Use of professional help/Motivation for treatment
Aster et al. (2018)Germany773Male: n = 688

Female: n = 85
Adolescents, AdultsClinical sample (treatment seeking PGs, relatives)N/A
Braun et al. (2014)Germany7,718Male: 83.9%

Female: 16.1%
AdultsClinical and general sampleDSM
Buchner et al. (2015)GermanyN/AN/AN/AClinical sampleICD-10
Echeburúa et al. (2011)Spain103Male: n = 52

Female: n = 51
AdultsClinical sampleDSM-IV
Harries et al. (2018)USA880Only reported for subgroupsAdultsClinical sample (treatment seeking PGs)MIDI, SCI-PG, PG-YBOCS
Kim et al. (2016)New Zealand150Male: n = 64

Female: n = 86
AdultsClinical samplePGSI
Kushnir et al. (2016)Canada207Male: n = 127

Female: n = 80
AdultsClinical samplePGSI
Nelson et al. (2006)USA2,256Male: n = 1,239–1,258

Female: n = 921–935
AdultsClinical sampleDSM-IV
Comorbidity
Bischof et al. (2013)Germany164Male: 74.4%

Female: 25.6%
AdultsClinical sampleCIDI
Bischof et al. (2015)Germany442Male: 83.9%

Female: 16.1%
AdultsClinical sampleCIDI
Blanco et al. (2006)USA43,093Only reported for participants with GDAdultsGeneral populationAUDADIS-IV
Bonnaire et al. (2017)France25,647Male: n = 12,504

Female: n = 13,142
Adults, Older AdultsGeneral populationCPGI
Boughton and Falenchuk (2007)Canada354Only femaleAdultsGeneral populationSOGS
Brand et al. (2019)USA591Male: n = 404

Female: n = 187
AdultsClinical sampleICD-9
Dannon et al. (2006)Israel78Male: n = 42

Female: n = 36
AdultsClinical sampleDSM-IV
Dash et al. (2019)Australia3,785Male: n = 1,365

Female: n = 2,420
AdultsGeneral populationNODS
Desai and Potenza (2008)USA43,039Male: n = 18,518

Female: n = 24,575
AdultsGeneral populationAUDADIS-IV
Díez et al. (2014)Spain96Male: n = 49

Female: n = 47
AdultsClinical sampleDSM-IV
Echeburúa et al. (2011)Spain103Male: n = 52

Female: n = 51
AdultsClinical sampleDSM-IV
Echeburúa et al. (2013)Spain206Male: n = 104

Female: n = 102
AdultsClinical and general populationSCI-PG, SCID-I
Ellenbogen et al. (2007)Canada5,313Male: n = 2,750

Female: n = 2,563
Adolescents, Young AdultsGeneral populationDSM-IV, GAQ
Fröberg et al. (2013)Sweden19,016Male: n = 8,447

Female: n = 10,569
Adolescents, Young AdultsGeneral populationN/A
Håkansson and Widinghoff (2020)Sweden327Male: n = 223

Female: n = 104
AdultsClinical samplePGSI
Håkansson et al. (2017)Sweden106Male: n = 85

Female: n = 21
AdultsClinical sampleDSM-5, ICD-10
Håkansson et al. (2018)Sweden2099Male: 77%

Female: 23%
AdultsClinical sampleICD-10
Jiménez-Murcia et al. (2009)Spain498Male: n = 439

Female: n = 59
AdultsClinical sampleSOGS, DSM-IV
Karlsson and Håkansson (2018)Sweden2099Male: n = 1,625

Female: n = 474
Adults, Older AdultsClinical sampleDSM-5, ICD-10
Lister et al. (2015)Canada150Male: n = 75

Female: n = 75
AdultsClinical sampleNODS
Luczak and Wall (2016)USA678Male: 50%

Female: 50%
Young AdultsGeneral populationSOGS
Moodie and Finnigan (2006)Scotland1827Male: n = 739

Female: n = 1,037
Young AdultsGeneral populationSOGS
Ronzitti et al. (2016)UK1,178Male: 92.5%

Female: 7.5%
AdultsClinical samplePGSI
Sanscartier et al. (2019)Canada624Male: 47.8%

Female: 52.2%
Young AdultsGeneral populationPGSI
Sundqvist and Rosendahl (2019)Sweden2010Male: 66/65%

Female: 34/35%
N/AClinical sample and general populationSOGS, PGSI
Suomi et al. (2014)Australia212Male: n = 105

Female: n = 107
AdultsClinical samplePGSI
Vogelgesang (2010)Germany200Male: n = 100

Female: n = 100
AdultsClinical sampleICD-10
Trauma
Boughton and Falenchuk (2007)Canada354Only femaleAdultsGeneral populationSOGS
Hodgins et al. (2010)Canada1,372Male: n = 602

Female: n = 770
Adults, Older AdultsGeneral populationPGSI, CIDI
Kausch et al. (2006)USA111Male: 91.9%

Female: 8.1%
AdultsClinical sampleGSRI, ASI
Ledgerwood and Milosevic (2015)Canada150Male: n = 75

Female: n = 75
AdultsGeneral population (TN with PG background)NODS, SOGS
Ledgerwood and Petry (2006)North America149Male: n = 72

Female: n = 77
AdultsClinical sample (treatment seeking PGs)NODS
Petry and Steinberg (2005)USA149Male: n = 72

Female: n = 77
AdultsClinical sample (treatment seeking PGs)SOGS
Roberts et al. (2017)UK3,025Only maleAdultsGeneral populationSOGS
Scherrer et al. (2007)USA1,675Only maleAdults, Older AdultsGeneral population (twins)DSM-IV
Shultz et al. (2016)USACases: 94

Controls: 91 (Relatives: 312)
Only reported for subgroupsAdultsGeneral populationSOGS, NODS
Violence
Afifi et al. (2010)USA3,334Only reported for subgroupsAdultsgeneral populationDSM-IV
Andronicos et al. (2015)N/A86Male: n = 41

Female: n = 45
AdultsClinical sampleSCID-I, SCID-II, SOGS
Cunningham-Williams et al. (2007)USA926Only femaleAdultsGeneral populationN/A
Dowling et al. (2014)Australia704Male: n = 364

Female: n = 320

Unspecified: n = 20
AdultsClinial sample (treatment seekingPGBBGS
Dowling et al. (2018)Australia4,153Male: 48.7%

Female: 51.3%
AdultsGeneral popualtionPGSI
Dowling et al. (2019)Australia1,109Male: 62.4%

Female: 37.6%
Adults, Older AdultsGeneral populationPGSI
Echeburúa et al. (2011)Spain103Male: n = 52

Female: n = 51
AdultsClinical sampleDSM-IV
Echeburúa et al. (2013)Spain206Male: n = 104

Female: n = 102
AdultsClinical and general populationSCI-PG, SCID-I
Fröberg et al. (2013)Sweden19,016Male: n = 8,447

Female: n = 10,569
Adolescents, Young AdultsGeneral populationN/A
Kausch et al. (2006)USA111Male: 91.9%

Female: 8.1%
AdultsClinical sampleGSRI, ASI
Korman et al. (2008)Canada248Male: 82.7%

Female: 17.3%
AdultsGeneral populationCPGI
Lee et al. (2012)USA515Male: n = 283

Female: n = 232
AdolescentsGeneral populationSOGS
Roberts et al. (2016)UK3,025Only maleAdultsGeneral populationSOGS
Suomi et al. (2019)Australia212Male: 49%

Female; 51%
AdultsClinical sample with family membersN/A
Vogelgesang (2009)Germany200Male: 50%

Female: 50%
AdultsClinical sampleICD-10
Criminality/delinquency
Abbott and McKenna (2005)New Zealand94Only femaleAdultsPrisonersSOGS
Granero et al. (2014)Spain2,309Male: 88.2%

Female: 11.8%
AdultsClinical sampleDSM-IV, SOGS
Ledgerwood et al. (2007)N/A231Male: n = 127

Female: n = 104
AdultsClinical sampleSOGS, ASI-G
Mestre-Bach et al. (2018)Spain273Only femaleAdultsClinial sample (treatment seekingPGs)DSM-IV-TR, SOGS
Vogelgesang (2009)Germany200Male: 50%

Female: 50%
AdultsClinical sampleICD-10

Note.

20-GA The 20 questions of the Gamblers Anonymous American Association.

ASI Addiction Severity Index.

ASI-G Addiction Severity Index Gambling Scale.

AUDADIS-IV Alcohol Use Disorder and Associated Disabilities Interview Schedule – DSM-IV version.

BBGS Brief Biosocial Gambling Screen.

CGI Clinical Global Impression-Severity scale.

CIDI Composite International Diagnostic Interview.

CPGI Canadian problem gambling index.

DSM-IV Pathological gambling diagnosed using the Diagnostic and Statistical Manual of Mental Disorders, 4th edition.

DSM-5 Pathological gambling diagnosed using the Diagnostic and Statistical Manual of Mental Disorders, 5th edition.

GAF Global Assessment of Functioning.

GAQ Gambling Activities Questionnaire.

GAS Gaming Addiction Scale.

GPSS of CAGI Gambling Problem Severity Subscale of the Canadian Adolescent Gambling Inventory.

GRSC Gambling Related Cognitions Scale.

GSAS Gambling Symptom Assessment Scale.

GSRI Gambler's Self-Report Inventory.

ICD-9 Pathological gambling diagnosed using the International Classification of Diseases and related Health Problems, 9th revision.

ICD-10 Pathological gambling diagnosed using the International Classification of Diseases and related Health Problems, 10th revision.

MIDI Minnesota Impulsive Disorders Interview.

NODS National Opinion Research Center (NORC) DSM Screen for Gambling Problems.

NODS-PERC National Opinion Research Center DSM-IV Screen for Gambling Problems - Preoccupation, Escape, Risked relationships and Chasing losses.

PGSI Problem Gambling Severity Index.

PG-YBOGS Pathological Gambling-Modification of the Yale-Brown Obsessive-Compulsive Scale.

PPGM problem and pathological gambling measure.

SCID-I Structured Clinical Interview for DSM IV Axis I Disorders.

SCID-II Structured Clinical Interview for DSM IV Axis II Personality Disorders.

SCI-GD Structured Clinical Interview for Gambling Disorder.

SCI-PG Structured Clinical Interview for Pathological Gambling.

SOGS South Oaks Gambling Screen.

VGS Victorian Gambling Screen.

Online. Overall, we found 11 studies investigating the prevalence of online gambling by sex (Chóliz, Marcos, & Lázaro-Mateo, 2019; Edgren, Castrén, Alho, & Salonen, 2017; Elton-Marshall, Leatherdale, & Turner, 2016; Floros, Siomos, Fisoun, & Geroukalis, 2013; Gainsbury et al., 2015; Gómez, Feijóo, Braña, Varela, & Rial, 2020; Griffiths, Wardle, Orford, Sproston, & Erens, 2009; Lelonek-Kuleta, Bartczuk, Wiechetek, Chwaszcz, & Niewiadomska, 2020; McCormack, Shorter, & Griffiths, 2014; Wu, Lai, & Tong, 2015; Yazdi & Katzian, 2017). The studies unanimously indicate that men are more likely to engage in online gambling. For example, Griffiths et al. (2009) and Chóliz et al. (2019) found that approximately 9% of men and only 3% of women had gambled online. Gómez et al. (2020) reported an even more pronounced sex difference of 11.5% vs. 1.4%. Griffiths et al. (2009) reported elevated prevalence rates of PG among persons who gamble online (5%). However, the group of people who gamble online was too small to be analyzed by sex. Data from Spain, where online gambling has been legalized since 2012, suggested that sex ratios for a mix of online and offline gambling are similar to previously reported sex ratios (Chóliz et al., 2019). See Table 1 at section ‘prevalence of online gambling’ for further information.

LGBTQI*. Overall, we found only three studies on the association between sexual orientation and the prevalence of pathological gambling. Two studies (Grant & Potenza, 2006; Richard et al., 2019) hinted at higher severity of gambling problems in the LGBTQI* community, whereas one study (Broman & Hakansson, 2018) found no evidence for elevated prevalence levels. As Richard et al. (2019) outlined, higher prevalence levels are in line with a generally higher risk for mental health and substance use problems in this group (see Table 1 at section ‘prevalence of LGBTQI*’).

Sociodemographic factors

We identified 11 studies providing information on sex differences for sociodemographic variables (Blanco et al., 2006; Bonnaire et al., 2016, 2017; Castrén, Kontto, Alho, & Salonen, 2018; Granero et al., 2009; Grant, Chamberlain, Schreiber, & Odlaug, 2012; Guillou-Landreat et al., 2016; Hing, Russell, Tolchard, & Nower, 2016; Jiménez-Murcia et al., 2020; Ronzitti, Lutri, Smith, Clerici, & Bowden-Jones, 2016; Vogelgesang, 2009). Education, income, employment status, and household composition were defined as variables of interest.

Two studies reported on education differences (Grant, Chamberlain, et al., 2012; Hing et al., 2016). Among a general population sample, Hing et al. (2016) found that men as well as women with PrG were more likely to have a lower education with less than 10 years of schooling, whereas Grant, Chamberlain, et al. (2012) found no educational differences among their clinical sample. Three studies included information on income differences (Blanco et al., 2006; Castrén, Kontto, et al., 2018; Granero et al., 2009). All showed that, on average, men with PrG had a higher monthly income than women with PrG. Concerning unemployment, two of three studies found women more likely to be unemployed (Granero et al., 2009; Ronzitti et al., 2016). Only Vogelgesang (2009) reported equivalent unemployment levels for men and women with PG, which, however, refer to a small sample size. Four studies on general population samples reported differences in household composition (Blanco et al., 2006; Bonnaire et al., 2016, 2017; Hing et al., 2016), whereas one study among a clinical sample found no differences (Grant, Chamberlain, et al., 2012). Men were more often found to be single and women to be widowed/divorced or separated. Furthermore, Hing et al. (2016) stated that women were more likely to be living in one-parent family households (see Table 1 at section ‘sociodemographic factors’).

Preferred gambling type

The 14 studies included indicate that women who gambled prefer non-strategic types of gambling (bingo, lottery, scratch cards), whereas men who gamble prefer more strategic games (casino games, horse race and sports betting) (Bonnaire et al., 2017; Leung & Tsang, 2011; Lopez-Gonzalez, Russell, Hing, Estévez, & Griffiths, 2020; Nong, Fong, Fong, & Lam, 2020; Nower & Blaszczynski, 2006; Odlaug et al., 2011; Potenza et al., 2006; Ronzitti et al., 2016; Stevens & Young, 2010; Svensson & Romild, 2014; Toneatto & Wang, 2009; van der Maas et al., 2018; Williams et al., 2013). However, age may be an important confounding factor as younger age is associated with a preference for strategic games and men who gamble tend to start gambling earlier (Bonnaire et al., 2017; Odlaug et al., 2011). Additionally, men who gamble were reportedly more likely to gamble on several games than women with PG (Stark, Zahlan, Albanese, & Tepperman, 2012). See Table 1 at section ‘preferred gambling type’ for further information.

Gambling motives

According to Flack and Stevens (2018), three main motives for gambling can be distinguished. People gamble for emotional (release of tension, stimulation, excitement), social (social recognition, “community” of gamblers), and monetary reasons (expectation to solve money problems). Of the nine studies identified (Clarke et al., 2007; Clarke & Clarkson, 2008; Echeburúa et al., 2011; Flack & Stevens, 2018; Grant & Kim, 2002; Hing et al., 2016; McCormack et al., 2014; Sundqvist, Jonsson, & Wennberg, 2016; Walker, Hinch, & Weighill, 2005), six found no statistically significant sex differences in any of these gambling motives. In contrast, three studies indicated sex differences (Echeburúa et al., 2011; McCormack et al., 2014; Walker et al., 2005). In a study on online gambling, female participants reported that they gambled significantly more often out of boredom, for free practice opportunities, and to spend less money compared with their male counterparts (McCormack et al., 2014). Walker et al. (2005) found that men who gamble in casinos endorsed risk-taking and cognitive self-classification more often than women as an important motive. Similar results are reported by Echeburúa et al. (2011), where men who gamble showed higher sensation seeking and impulsiveness than women (see Table 1 at section ‘gambling motives’). Age specific differentiations were not available, as the studies mainly referred to adults.

Severity

Most of the eight included clinical studies showed higher levels of problem gambling severity for women than for men (Grant, Chamberlain, et al., 2012; Grant, Odlaug, & Chamberlain, 2017; Håkansson & Widinghoff, 2020; Jiménez-Murcia et al., 2020; Kim, Hodgins, Bellringer, & Abbott, 2016; Ronzitti et al., 2016). These higher levels of severity concerned the gambling disorder itself as well as accompanying burdens such as elevated anxiety or a more general psychopathology (Granero et al., 2009; Grant, Chamberlain, et al., 2012). However, there are also contradictory results. Another study stated an equal severity of PG for both sexes with no differences concerning SOGS total score and total DSM-IV reported criteria (Granero et al., 2009). Furthermore, Jiménez-Murcia et al. (2016) found higher severity scores for men in association with their early onset of PrG, suggesting that the onset of PrG mediates between sex and severity (Jiménez-Murcia et al., 2016). See Table 1 at section ‘severity’ for further information.

Progression of gambling problems

Six studies tackling the progression of gambling problems were included. Typical course-related factors such as age at gambling onset, first symptoms of PG, and onset of a PG diagnosis generally occurred earlier in men than in women (Carneiro et al., 2014; Grant, Odlaug, & Mooney, 2012; Slutske et al., 2014). This finding was also observed for a longitudinal study among young adults below the age of 25 by Edgerton, Melnyk, and Roberts (2015).

Two studies with treatment-seeking samples found a shorter time interval from the onset of gambling to the first occurrence of manifest problems with gambling for women compared with men who gamble (Grant, Odlaug, & Mooney, 2012; Nelson, LaPlante, Labrie, & Shaffer, 2006). However, Slutske et al. (2014) found no evidence for the telescoping effect in a general population sample.

One cross-sectional study found a temporal difference in the occurrence of comorbid disorders (Haw & Holdsworth, 2016). Women with PG retrospectively reported that they had experienced other disorders (e.g., mood and substance abuse disorders) before the first onset of PrG, whereas men indicated it to be the other way round. It should be noted that the PGSI was used here, which is usually recommended in a general population setting (Holtgraves, 2009) (see Table 1 at section ‘progression of gambling problems’).

Use of professional help/motivation for treatment

Sex differences in motivation for treatment and treatment seeking have hardly been examined so far. The existing evidence in the eight included studies is mixed. Echeburúa et al. (2011), Harries, Redden, and Grant (2018), and Kushnir, Godinho, Hodgins, Hendershot, and Cunningham (2016) found no differences between men and women with PrG and PG regarding dropout rates and general rate of treatment seeking. In contrast to these studies, a German study on the outpatient sector by Braun, Ludwig, Sleczka, Bühringer, and Kraus (2014) identified lower chances of being in treatment for women, which is consistent with another German study on inpatient treatment for gamblers (Buchner et al., 2015). Furthermore, studies that dealt with the use of telephone gambling hotlines reported significantly more use of subsequent treatment services among men than among women, which was also apparent for adolescents (Aster, Quack, Wejbera, & Beutel, 2018; Kim et al., 2016). However, women tend to seek therapeutic support sooner after recognizing their gambling problems than men. Usually, men start gambling earlier than women, but the time span between the occurrence of gambling problems and treatment seeking was longer. The authors also showed an interaction effect of initiation age and time to treatment. Earlier age of initiation was associated with a longer period from recognition of having a problem until treatment seeking. This relationship was stronger for men than for women (Nelson et al., 2006).

Regarding motivation for treatment, a study conducted by Kushnir et al. (2016) found that men exhibited higher trait detachment, whereas women tended to have higher scores on guilt proneness and shame, which is of interest as these factors can be relevant regarding the motivation for change. Despite these differences, the authors did not find sex effects concerning motivation for change (see Table 1 at section ‘use of professional help/motivation for treatment’).

Comorbidity

In general, PG was often accompanied by mental and substance-related comorbidities [e.g., 73% of persons with PG have a current other psychiatric diagnosis (Håkansson, Karlsson, & Widinghoff, 2018); the lifetime prevalence for any other psychiatric disorder for people with PG is 96.3% (Bischof et al., 2013). However, studies indicated that general comorbidity rates are higher for women (Brand, Rodriguez-Monguio, & Volber, 2019; Håkansson, Mårdhed, & Zaar, 2017, 2018; Suomi, Dowling, & Jackson, 2014).

Most evidence for higher comorbidity rates in women was found for affective disorders (Bischof et al., 2013; Blanco et al., 2006; Boughton & Falenchuk, 2007; Brand et al., 2019; Dannon et al., 2006; Desai & Potenza, 2008; Díez et al., 2014; Echeburúa et al., 2011; Erbas & Buchner, 2012; Håkansson et al., 2018; Jiménez-Murcia et al., 2009; Lister, Milosevic, & Ledgerwood, 2015; Moodie & Finnigan, 2006; Ronzitti et al., 2016; Sundqvist & Rosendahl, 2019; Vogelgesang, 2010). Only two studies reported no sex differences for affective disorders with these findings referring to young people and general population settings (Echeburúa et al., 2013; Ellenbogen, Derevensky, & Gupta, 2007). An equally strong sex difference as for affective disorders could be found for anxiety disorders (Blanco et al., 2006; Boughton & Falenchuk, 2007; Dannon et al., 2006; Echeburúa et al., 2011; Erbas & Buchner, 2012; Håkansson et al., 2018; Jiménez-Murcia et al., 2009; Sundqvist & Rosendahl, 2019; Ronzitti et al., 2016; Vogelgesang, 2010). However, one general population study found no sex differences for anxiety disorders (Echeburúa et al., 2013). As it is typical for other psychological disorders, suicidality was found to be more prevalent in gambling women than in men (Bischof et al., 2015; Boughton & Falenchuk, 2007; Fröberg, Hallqvist, & Tengström, 2013; Sanscartier, Shen, & Edgerton, 2019; Sundqvist & Rosendahl, 2019). However, more committed suicides were reported for men (Karlsson & Håkansson, 2018).

The evidence for a correlation between PG and alcohol abuse/dependence was quite consistent for men but not for women (Blanco et al., 2006; Dannon et al., 2006; Dash et al., 2019; Echeburúa et al., 2011, 2013; Erbas & Buchner, 2012; Fröberg et al., 2013; Luczak & Wall, 2016; Ronzitti et al., 2016; Sanscartier et al., 2019; Suomi et al., 2014; Sundqvist & Rosendahl, 2019). Research concerning nicotine use or dependence and PG showed conflicting results, which seem to depend on the sample type. While general population studies indicated higher rates for gambling women (Bonnaire et al., 2017; Boughton & Falenchuk, 2007; Desai & Potenza, 2008), clinical studies reported no sex differences (Ronzitti et al., 2016; Vogelgesang, 2010).

Regarding other substance-related disorders, six studies showed a correlation with gambling for men but not for women (Brand et al., 2019; Dannon et al., 2006; Echeburúa et al., 2011; Erbas & Buchner, 2012; Jiménez-Murcia et al., 2009; Ronzitti et al., 2016). One study, addressing adolescents, contradicted this finding and found higher rates of using hard drugs for girls than for boys (Ellenbogen et al., 2007). No sex differences in substance use were found in three studies (Echeburúa et al., 2013; Håkansson et al., 2018; Håkansson & Widinghoff, 2020). See Table 1 at section ‘comorbidity’ for further information.

Trauma

Nine studies regarding trauma were included in this review. For men as well as women with PrG and PG, there was evidence for higher odds of traumatic life events than for people without gambling problems (Boughton & Falenchuk, 2007; Hodgins et al., 2010; Ledgerwood & Petry, 2006; Ledgerwood & Milosevic, 2015; Roberts et al., 2017; Scherrer et al., 2007; Shultz, Shaw, McCormick, Allen, & Black, 2016). These studies further observed that past experience of maltreatment was more frequent in women with PG. Similarly, most clinical studies showed that rates of physical, emotional, or sexual abuse in childhood as well as trauma in adulthood were considerably higher for women who gamble than for their male counterparts (Kausch, Rugle, & Rowland, 2006; Petry & Steinberg, 2005). In a general population study, people with gambling disorder and a comorbid post-traumatic stress disorder (PTSD) were less likely to be men than women (Ledgerwood & Milosevic, 2015). In contrast to clinical studies, the results of general population studies were less equivocal. With regard to the odds of maltreatment of men and women with PrG, general population studies suggested that there is no difference (Hodgins et al., 2010). See Table 1 at section ‘trauma’ for further information.

Violence

We found 16 studies that explored the relationship between sex, gambling, and violence (Andronicos et al., 2015; Afifi, Brownridge, MacMillan, & Sareen, 2010; Cunningham-Williams, Abdallah, Callahan, & Cottler, 2007; Dowling et al., 2014, 2016, 2018, 2019; Echeburúa et al., 2011; Fröberg et al., 2013; Kausch et al., 2006; Korman et al., 2008; Lee, Storr, Ialongo, & Martins, 2012; Roberts et al., 2016; Suomi et al., 2019; Vogelgesang, 2009, 2013). Pathological gambling was found to be associated with a higher likelihood of being the victim and the perpetrator of violence (psychological, physical, and sexual violence) (Afifi et al., 2010; Roberts et al., 2016; Suomi et al., 2019). It is noteworthy that the prevalence of violence was unusually high among men and women with PrG and PG (compared with the general population). In their systematic review, Dowling et al. (2016) reported that around 38% were victims of violence and 37% perpetrators. In most cases, men and women who gambled were both victims and perpetrators, indicating reciprocal violence (Afifi et al., 2010; Suomi et al., 2019). Suomi et al. (2019) and Korman et al. (2008) found that around 60% of people with gambling problems experienced violence during the past year. For women who gambled, Echeburúa et al. (2011) reported an even higher number of 68.6%, compared with only 9.8% in a non-gambling control group. Although there was robust evidence of the association between violence and gambling for men and women, the research findings on sex differences in scale were mixed. While Suomi et al. (2019) did not find differences between men and women, Dowling et al. (2014) found that women were more likely to be victims (2.1 times) than men and to report both victimization and perpetration (1.6 times) more often than men. Similar findings were reported by Kausch et al. (2006) and Andronicos et al. (2015). In a small clinical sample, Vogelgesang (2009) found higher rates of physical maltreatment and specifically sexual assaults for women (See Table 1 at section ‘violence’).

Criminality/delinquency

Overall, we found six studies exploring sex differences in criminality (Abbott & McKenna, 2005; Banks, Waters, Andersson, & Olive, 2020; Granero et al., 2014; Ledgerwood, Petry, Weinstock, & Morasco, 2007; Mestre-Bach et al., 2018; Vogelgesang, 2009). In relationship to gambling, mostly income-generating crimes were reported. These crimes included petty theft, fraud, and forgery (Laursen, Plauborg, Ekholm, Larsen, & Juel, 2016). Overall prevalence rates ranged from 14% to 30% (Granero et al., 2014; Ledgerwood et al., 2007). No sex differences were observed. Only in a very small clinical sample women who gambled reported criminal acts in 10% of the cases, whereas men who gambled had committed crimes in 30% of the cases (Vogelgesang, 2009).

Another topic of interest concerning criminality was the high prevalence of PG in forensic populations. In their systematic review, Banks et al. (2020) found that 10.4–73% of imprisoned men met the criteria for PG, whereas only 5.9–45% of imprisoned women did so. These numbers show that prevalence rates among inmates were significantly higher than in the general population and higher for men than for women (see Table 1 at section ‘criminality/delinquency’).

Discussion

Lack of gender sensitive research

The most important result of our extensive literature search was that studies measuring gender are still scarce. Our initial goal, to explicitly include gender differences and not only focus on sex differences could thus not be met. The studies on LGBTQI* were the only studies to include sexual orientation. In the other studies the terms sex and gender were used interchangeably without differentiating between the two concepts. As the studies failed to measure gender and only reported sex, the results of this review are limited to sex differences. This result was rather surprising as even more recent studies showed little awareness of the problem. An inconsistent use of the terms sex and gender, poor measuring of sex and gender, and a lack of gender- and sex-specific reporting can be considered as a major impediment concerning reviews on sex and gender differences (Blakeman, 2020). Future studies correctly assessing sex and gender are needed in order to highlight gender differences and not only sex differences (Clayton & Tannenbaum, 2016). As a generally accepted standard of measuring and reporting gender and related concepts is still missing, it will be a challenge to implement new ways of assessing gender and embrace the linked higher level of complexity when it comes to data analysis and interpretation of results (Blakeman, 2020).

The minimum standard should be, to separately assess and report both sex and gender (e.g., see most applied gender identity measurements in GenIUSS, 2013). As gender is a complex construct, it would be advisable to use multidimensional instruments [e.g., gender scale by Pelletier et al. (2015) or Lindqvist, Gustafsson Sendén, & Renström, 2021].

We are aware that including gender measures might be challenging, however we consider it to be necessary. As gambling is a phenomenon shaped by environmental and sociocultural factors and presumably not primarily by biological sex differences, investigating gender differences could yield new and surprising results.

One example is the preliminary result, that prevalence rates for PG might be elevated in the LGBTQI* community. Expanding research on differences by gender and sexual orientation might also yield new insights into the motives underlying observable differences. For instance, gambling is often used as a coping mechanism to deal with discrimination and victimization experiences linked to a non-heteronormative sexual orientation (Richard et al., 2019).

Furthermore, existing data on substance use disorder (SUD) suggest that results for prevalence rates or comorbidities may differ depending on whether sex or gender is considered. Data from the Word Health Organization indicate that prevalence differences for SUD narrow in societies with less gender role traditionality (Seedat et al., 2009). Given these findings it is reasonable to assume that including gender aspects in gambling research would add value to the current picture.

Sex differences

Despite not being able to report on gender differences, the review yielded some important results concerning sex differences, which will be discussed in the following.

Overall, most results on sex differences concerning persons with PrG and PG were mixed. However, the review could identify a few areas with rather robust evidence of sex differences. Compared to women, men generally showed higher prevalence rates, started gambling at an earlier age, reported a higher monthly income, played a larger variety of chance-based games, and showed a tendency toward strategic forms of gambling. These results are mostly in line with the review by Wenzel and Dahl (2009). Concerning prevalence rates there is even some evidence that the sex gap might be narrowing (Castrén, Heiskanen, & Salonen, 2018; McCarthy et al., 2018). Possible explanations are emerging games and gambling environments designed to appeal specifically to women.

For quite a few of the studied characteristics, the number and quality of studies were limited, indicating the need for further research. Especially in areas such as LGBTQI*, online gambling and the use of help/motives for help seeking, a shortage of studies was observed. Concerning sex differences, some further reasons for ambiguous results must be discussed. Contradictory findings in areas such as motives for gambling, severity, or comorbidity might be an artifact of varying study quality. Besides small sample sizes and heterogeneous definitions of PrG and PG (different diagnostic instruments), important confounding factors, such as progress of severity and sample age, were not considered in all studies. Previously claimed differences in gambling motives are an example. In this case, the assumption that women gamble to regulate emotions, whereas men gamble for economic reasons might result from a failure to account for the severity and progression of gambling problems. Rather than being a gender effect, monetary reasons and social recognition become less important and emotional regulation becomes the primary goal as the addiction progresses. Reports about differences in gambling motivations could thus be seen as artifacts of confounding factors such as the severity of problem gambling and addiction progress.

Even areas with apparently clear sex differences (prevalence rates) show a more nuanced picture when scrutinized in detail. The convergence of prevalence rates with increasing age is one example of the complex interplay of sex with further sociocultural factors (e.g., sample age, country in which the study was conducted, sample type: general population vs. treatment-seeking population).

This finding is in line with the review by Merkouris et al. (2016), lending support to the gender as proxy theory (Nelson et al., 2006). Although sex is an important factor in explaining differences in gambling characteristics, the size of its contribution might sometimes be overestimated by a failure to account for further psychosocial characteristics. When factors such as age at initiation, socioeconomic status, cultural background, family history of addiction problems, etc. are considered, differences attributable to sex might diminish.

In line with the existing literature, we found mixed evidence for gambling-related sex differences in areas such as violence or acquisitive crime. Although women were more likely to be the victim and less likely to be the perpetrator in some of the studies, they were also generally found to have a higher proportion of delinquents or offenders among them. The ambiguous results are certainly caused by the very heterogeneous samples, survey settings and possibly biased by a failure to distinguish between minor violent acts and actual health-threatening assaults, but there is a need to expand research in this area. For clinicians, it might be important to keep in mind that a considerable number of men as well as women with PG are both perpetrators and victims of violence.

Concerning treatment, the review findings clearly indicate that factors such as trauma and violence should be actively queried for both sexes. Trauma appears to be a major risk factor for developing a gambling disorder and should therefore necessarily be considered in the development of prevention and treatment strategies. Especially women with more severe experiences of traumatic life events might be more likely to seek treatment than women with less severe histories (Hodgins et al., 2010).

Given the low utilization rates among women who gamble, it is important to elaborate and expand therapy services that are more suitable for women (Aster et al., 2018; Loy, Daniel, Bickl, Schwarzkopf, & Kraus, 2021; Kim et al., 2016). As prevention and therapy approaches are often geared towards a male clientele, it is necessary to expand the orientation of the offers in a more inclusive way, addressing feelings of shame and stigmatization among women.

Conclusions and further research

In conclusion, the review could identify a few areas with robust evidence of sex differences (e.g., men show higher prevalence rates, women start gambling later in life). Concerning treatment, PTSD and being the victim of violence should be checked for both sexes, but especially for women. Women's access to therapy services should be improved.

As studies on gender differences are currently scarce, future studies should focus on gender and report both sex and gender. Furthermore, relevant confounding factors such as severity, sample age should be considered, and homogeneous and psychometric valid instruments used.

Funding sources

This study was conducted in the context of the Bavarian Coordination Centre for Gambling Issues (Bayerische Landesstelle Gluecksspielsucht (LSG)). The LSG is funded by the Bavarian State Ministry of Public Health and Care Services. The State of Bavaria provides gambling services (lotteries, sports betting and casino games) within the State gambling monopoly via the State Lottery Administration and provided funding for the Bavarian Coordination Centre for Gambling Issues as an unrestricted grant.

Authors’ contribution

CG, AB, SH and JL designed the study. CG, AB, SH, JL and LH acquired the data to be reviewed. CG, AB, SH, JL and LH conducted the analysis and interpretation of the data. All authors critically contributed to earlier drafts of the manuscript. CG, AB, JL and LH approved the final version of the paper.

Conflict of interest

The authors report no financial or other relationship relevant to the subject of this article.

Acknowledgements

The authors express their heartfelt thanks to their colleagues Sonja Schröder, Pia Wullinger, Larissa Schwarzkopf, Norbert Wodarz, and Ludwig Kraus, who provided advice and support in conducting the study or drafting this paper.

References

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