Open Access

Problem gambling among ethnic minorities: results from an epidemiological study

Asian Journal of Gambling Issues and Public Health20177:7

https://doi.org/10.1186/s40405-017-0027-2

Received: 26 May 2017

Accepted: 13 August 2017

Published: 7 September 2017

Abstract

A few studies have examined gambling behavior and problem gambling among minorities and reported higher rates of both participation and gambling problems among particular minority groups in comparison to Whites who gamble. The present study utilized a representative, epidemiological sample of adults in New Jersey to explore gambling behavior, gambling problem severity, substance use, problem behavior, and mental health issues among minorities. Univariate analyses were conducted, comparing Whites (n = 1341) to respondents who identified as Hispanic (n = 394), Black (n = 261), or Asian/other (n = 177). Overall, the highest proportion of Hispanics were high-risk problem gamblers. Hispanic participants were also significantly more likely than other groups to use and abuse substances and to report mental health problems in the past month, behavioral addictions, and/or suicidal ideation in the past year. Primary predictors of White high risk problem gamblers were being young and male with friends or family who gambled, fair to poor health status, substance use, gambling once a week or more both online and in land-based venues, and engaging in a number of gambling activities. In contrast, gender was not a predictor of minority high risk problem gamblers, who were characterized primarily by having friends or family who gambled, gambling online only, having a behavioral addiction and playing instant scratch-offs and gaming machines. Implications for research and practice are discussed.

Background

Studies have consistently reported high rates of problem gambling among racial and ethnic minorities compared to Whites, though findings differ by geographic location and socioeconomic status: ([Native American] Volberg and Abbott 1997; Zitzow 1996a, b; [Asian] (Marshall et al. 2009; Petry et al. 2003; Toyama et al. 2014); [Hispanic or Latino] Barry et al. 2011a; Welte et al. 2001; [Black or African American] Barnes et al. 2009; Barry et al. 2011b; Welte et al. 2008).

A majority of studies focused on ethnicity investigated rates of gambling and problem gambling among Blacks, including African Americans. Results of a large nationally-representative study found that Blacks had twice the rate (2.2%) of disordered gambling compared to Whites and lower scores on general health measures; they were also more likely to be women in the lowest income brackets (Alegria et al. 2009). Similar findings have been reported regarding Black youth, who were significantly more likely than white youth to engage in heavy gambling (Barnes, et al. 2009). Overall, being young, male, and non-Hispanic Black was associated with high rates of gambling disorder in the U.S. National Comorbidity Survey Replication (NCS-R) data (Kessler et al. 2008). These findings generally mirror sociodemographic characteristics and comorbidity patterns found in earlier studies (Petry et al. 2005; Welte et al. 2001) as well as in special sub-groups of Black gamblers ([hotline callers] Barry et al. 2008; [casino self-excluders] Nower and Blaszczynski 2006; [homeless individuals] Nower et al. 2015; [veterans] Stefanovics et al. 2017). Welte et al. (2017) have noted that adults living in disadvantaged neighborhoods reported the most problem gambling symptoms, however studies have yet to explore the predictors of problem gambling versus other adaptive and maladaptive behaviors in these groups apart from religiosity, which serves as a protective factor (Welte et al. 2017).

There is scant research involving Hispanics/Latinos and gambling. The few studies that exist are small-scale investigations of specific sub-groups. One general population survey reported that Hispanics/Latinos with subthreshold gambling problems were more likely to have comorbid mood, anxiety, substance use, and personality disorders than White participants. In another study of Latino American veterans, Westermeyer et al. (2005) found that the lifetime prevalence rate of disordered gambling was 4.3%, nearly four times higher than in the general population. The study further noted that gambling disorder was comorbid with high rates of major depressive (14.1%), alcohol (22.9%), and posttraumatic stress (12.2%) disorders in that sample. More than half of the undocumented Mexican immigrants surveyed in a small study in New York City reported having gambled, and a majority of those gamblers played scratch and win tickets or the lottery (Momper et al. 2009). Those who sent money home to their families or had lived in the United States more than 12 years and those who reported 1–5 days of poor mental health in the past 30 days were most likely to gamble.

Research among Asian gamblers has been limited, possibly because of the tension between the permissive attitude toward gambling and the increased stigma ascribed to those who gamble problematically in Asian groups (Dhillon et al. 2011). In the U.S., studies have identified higher rates of gambling and problem gambling among Asian subgroups, such as Southeast Asian and Cambodian refugees in the U.S., who reported rates of gambling disorder as high as 59% (Petry et al. 2003) and 13.9% (Marshall et al. 2009), respectively. Similarly, another study found that, among college students, Chinese students reported the highest rates of gambling problems followed by Koreans then Whites. The most significant predictors of problem gambling in that study were being Chinese or Korean and male, and having an alcohol or drug problems (Luczak and Wall 2016).

The culturally-based motivation to gamble and the risk and protective factors that fuel or arrest the progression toward problem gambling in ethnic sub-groups are likely complex and varied. Some researchers have suggested that the stress of acculturation may play a significant role. A recent study, examining differences in gambling behavior among first, second, and third generation immigrants from a diverse collection of world regions (Africa, Asia, Europe, and Latin America), found the lowest rates of gambling participation among Latin Americans, followed by Africa, Asia, and Europe, which had the highest rates. First-generation immigrants had lower rates of gambling prevalence and problem gambling when compared to second and third generation immigrants or native-born Americans. In addition, the study found that immigrants who arrived in the U.S. as children (12 or younger) gambled more frequently than those arriving as adolescents or adults (Wilson et al. 2015). Issues surrounding acculturative stress may also play a role in the development of gambling problems among youth. A recent study found that rates of at-risk or problem gambling among first generation adolescent immigrants were twice as high as their non-immigrant peers, particularly if they lived apart from their parents (Canale et al. 2017).

In addition to the influence of acculturation, other theorists have suggested that biology, values and beliefs also play a role. Chamberlain et al. (2016) suggested that inflated rates of problem gambling among some ethnic and racial groups may be due, in part, to neurocognitive differences among groups, as measured by differing rates of compulsivity, errors on memory and set-shifting tasks, and delay aversion, which they found were higher in Black versus White participants in one study. Other researchers underscore the influence of values and beliefs inherent in specific cultural groups or sub-groups in the progression and maintenance of problem gambling behavior (Alegria et al. 2009; Raylu and Oei 2004; Sacco et al. 2011). For example, certain Asian cultures consider gambling activities to be a part of their lifestyle and tradition (Clark et al. 1990; Raylu and Oei 2004). In other ethnic groups and cultures (e.g. Native Americans), the concepts of fate and a reliance on magical thinking may encourage gambling behavior in the same way as cognitive distortions do in pathological gamblers (Hardoon et al. 2001; Zitzow 1996a, b). Issues of social isolation, language barriers, and access to employment must also be clinically considered as factors which can drive immigrant populations towards pathological gambling behavior (Ngai and Chu 2001; Tse 2003).

To date, a notable exception has been found in the Hispanic native born and immigrant communities where, despite the adversity of poverty, lack of education, and social discrimination, rates of pathological and problem gambling are below that of the White majority (Alegria et al. 2009). This phenomenon seems to parallel the “Hispanic paradox” (Scribner 1996) documented in health outcome studies, where Hispanics have better health outcomes despite the challenges of low socioeconomic status and barriers to accessing healthcare (Grant et al. 2004; Scribner 1996; Vega et al. 1998).

Given the lack of clarity surrounding differences among minority groups and between minority and White gamblers, the purpose of this study is to explore differences in the characteristics and behaviors of non-problem gamblers compared to high-risk problem gamblers across different ethnic groups.

Methods

Participants

The study utilized a sub-set of 2173 New Jersey residents over 18 who endorsed at least one gambling activity in the past year from a larger epidemiologic study of 3634 participants. The remaining 1461 participants reported no involvement in any gambling activities in the past year and were excluded from the analyses. Data coding and analyses were conducted using SPSS version 24.

Measures

The present study incorporated data collected through an epidemiological survey conducted across the state of New Jersey that stratified its sampling method to accurately reflect the demographic makeups of each region of the state. Sections of the survey produced data on the following variables: (a) demographics (gender, age, race/ethnicity, education level, household income, immigration status, and relationship status); (b) substance use (tobacco use, alcohol use, illegal drug use, problems and treatment seeking with substances, behavioral addictions, and binge drinking); (c) mental health and physical health (overall stress level, overall level of happiness, overall health, experiences of mental health problems in the past 30 days and 12 months, suicidal ideation, and suicidal attempts in the past year); (d) gambling activities participated in the past year (lottery, bingo, scratch offs, sports betting, horse race track betting, poker, casino table games, other games of skill, and gaming machines); (e) non-gambling activities participated in the past year (high risk stocks and daily fantasy sports); (f) gambling behavior (frequency of participation, amount of money spent, venue preference for gambling, and online gambling participation across all previously mentioned forms).

Problem Gambling Severity Index (PGSI) of the Canadian Problem Gambling Index (CPGI, Ferris and Wynne 2001 ) This 9-item instrument was used to assess gambling status. Respondents indicate the extent to which an item applies to them using a four-point Likert scale ranging from 0 (never) to 3 (almost always). Scores are totaled in accordance with Ferris and Wynne’s (2001) guidelines: 0 indicates no risk; 1–2 low risk; 3–7 moderate risks; and 8–27 problem gambling, respectively. Ferris and Wynne (2001) reported satisfactory scale reliability (α = 0.84). For the purpose of the logistic regression analyses, a non-problem gambler was classified as any scoring 0 on the PGSI and “at-risk” gamblers were classified as any participant scoring 3 or higher on the PGSI.

Procedure

The data was collected both by telephone (cell and landline phones) and Internet to address limitations inherent in either methodology alone. Stratified sampling was used in both sub-samples to ensure demographic characteristics of age, gender, and race/ethnicity were reflective of the New Jersey population.

Results

Univariate analyses

Univariate comparisons among problem severity categories were performed for gender, age, race/ethnicity, education level, marital status, household income, and employment status. Table 1 presents the distribution and statistical significance of explanatory variables by PGSI category. The association between the PGSI and each explanatory variable was assessed using Chi-squared Test of Independence. No socioeconomic variables showed a significant association with the PGSI. High risk of problem gambling was significantly associated with age (younger), gender (male), race/ethnicity (Hispanic and Asian/other), marital status (married), self-assessed health in the past year (Excellent), and past year stress (high). Non-problem gambling was significantly associated with age (older), gender (female), race/ethnicity (White), marital status (divorced/separated), self-assessed health in the past year (good/fair) and past year stress (low).
Table 1

Demographic breakdown of non-problem (n = 1510) and at-risk problem gamblers (n = 663)

Variable

Non-PG

 

Low risk PG

 

Moderate risk PG

 

High risk PG

 

Total

n

%

n

%

n

%

n

%

n (% of total)

Age*

21–24

92

6.1

34

12.3

28

14.8

38

19.4

192 (8.8)

25–34

237

15.7

65

23.5

52

27.5

73

37.2

427 (19.6)

35–44

312

20.6

49

17.7

50

26.5

55

28.1

466 (21.4)

45–54

332

22.0

66

23.8

32

16.9

21

10.7

451 (20.8)

55–64

243

16.1

30

10.8

12

6.3

7

3.6

292 (13.4)

65+

295

19.5

33

11.9

15

7.9

2

1.0

345 (15.9)

Gender*

Male

695

46.0

150

54.2

120

63.2

136

69.4

1101 (50.6)

Female

815

54.0

127

45.8

70

36.8

60

30.6

1072 (49.4)

Race/ethnicity*

White

1016

67.3

155

60.0

90

47.4

80

40.8

1341 (61.7)

Hispanic

245

16.2

40

14.4

49

25.8

60

30.6

394 (18.1)

Black

155

10.3

51

18.4

27

14.2

28

14.3

261 (12.0)

Asian/other

94

6.2

31

11.2

24

12.6

28

14.3

177 (8.2)

Marital status*

Married or living w/partner

937

62.0

162

58.5

108

56.8

139

70.9

1346 (62.0)

Divorced, separated, Widowed

241

16.0

42

15.2

15

7.9

19

9.7

317 (14.6)

Single (never married)

332

22.0

73

26.3

67

35.3

38

19.4

510 (23.4)

Health status (past year)*

Excellent

271

17.9

35

12.6

38

20.0

59

30.1

403 (18.5)

Good/fair

1051

69.6

197

71.2

118

62.1

111

56.6

1477 (68.0)

Poor

188

12.5

45

16.2

34

17.9

26

13.3

293 (13.5)

Overall stress level (past year)*

Low

355

23.5

56

20.2

37

19.5

30

15.3

478 (22.0)

Moderate

1020

67.6

200

72.2

137

72.1

121

61.7

1478 (68.0)

High

135

8.9

21

7.6

16

8.4

45

23.0

217 (10.0)

Yearly household income

Less than $15,000

65

4.3

14

5.1

9

4.7

13

6.6

101 (4.7)

$15,000–29,999

137

9.1

19

6.9

30

15.8

18

9.3

204 (9.4)

$30,000–49,999

207

13.7

53

19.1

28

14.7

21

10.7

309 (14.2)

$50,000–69,999

256

17.0

54

19.5

42

22.1

44

22.4

396 (18.2)

$70,000–99,999

305

20.2

57

20.6

36

18.9

41

20.9

439 (20.2)

$100,000–124,999

198

13.1

36

13.0

18

9.6

34

17.3

286 (13.2)

$125,000–149,999

120

7.9

15

5.4

10

5.3

14

7.2

159 (7.3)

$150,000 or more

222

14.7

29

10.4

17

8.9

11

5.6

279 (12.8)

Education level

Less than high school or GED

17

1.1

10

3.6

5

2.6

12

6.1

44 (2.0)

High school diploma or GED

294

19.5

60

21.7

34

18.0

33

16.8

421 (19.4)

Some college (less than 1 year)

114

7.5

30

10.8

23

12.1

18

9.2

185 (8.5)

Some college (more than 1 year)

187

12.4

35

12.6

19

10.0

15

7.7

256 (11.8)

Associate’s degree

145

9.6

17

6.1

15

7.9

22

11.2

199 (9.1)

Bachelor’s degree

465

30.8

89

32.1

55

28.9

44

22.4

653 (30.1)

Master’s degree

219

14.5

27

9.7

25

13.2

33

16.8

304 (14.0)

Professional degree

38

2.5

6

2.2

9

4.7

13

6.6

66 (3.0)

Doctorate degree

31

2.1

3

1.2

5

2.6

6

3.2

45 (2.1)

Employment status

Employed for Wages

843

55.8

173

62.5

120

63.2

127

64.7

1263 (58.2)

Self-employed

121

8.0

24

8.7

15

7.9

25

12.7

185 (8.5)

Out of work (less than 1 year)

34

2.3

5

1.8

2

1.1

7

3.6

48 (2.2)

Out of work (more than 1 year)

32

2.1

7

2.5

9

4.7

5

2.6

53 (2.4)

Homemaker

90

6.1

14

5.1

6

3.2

12

6.1

122 (5.6)

Student

61

4.0

15

5.3

17

8.9

7

3.6

100 (4.6)

Retired

283

18.7

31

11.2

15

7.8

6

3.1

335 (15.4)

Unable to work

46

3.0

8

2.9

6

3.2

7

3.6

67 (3.1)

* p ≤ .01

Additionally, Table 2 presents associations between race/ethnicity and gambling frequency, preferred gambling venue(s), participation in individual gambling activities, five measures of substance use, and three measures of mental health. Race/ethnicity was significantly associated with both high (Hispanics) and low frequency (Whites) gambling, land-based only gambling (Whites), and gambling both online and in land-based venues (Hispanics). Looking at specific gambling activities, race/ethnicity was significantly associated with instant scratch-off ticket play, bingo, sports betting, horse race track betting, live poker, live casino table games and other games of skill. Asians were more likely than other ethnicities to have participated in bingo within the past year, while Hispanics preferred sports betting, horse race track betting, live poker games, live casino table games and other games of skill. Hispanic participants were distinguished by their answers to questions pertaining to substance use and mental health issues. Hispanic respondents were more likely than the other ethnicities to endorse tobacco use, binge drinking, illegal drug use and problems due to drug or alcohol use in the past year. Hispanic participants were also more likely than other groups to endorse a mental health problem in the past 30 days, having a behavioral addiction and/or suicidal ideation in the past year.
Table 2

Gambling, substance use, and mental health by ethnicity

Variable

White

Hispanic

Black or African American

Asian/other

Total

n (1341)

%

n (394)

%

n (261)

%

n (177)

%

n (% of total)

Gambling frequency**

Low

478

66.1

114

15.8

75

10.4

56

7.7

723 (100.0)

Medium

367

63.1

92

12.7

81

13.9

42

7.2

582 (100.0)

High

496

57.1

188

21.7

105

12.1

79

9.1

868 (100.0)

Preferred gambling venue(s)***

Land-based only

1067

66.2

244

15.1

198

12.3

104

6.4

1613 (100.0)

Online only

66

57.4

26

22.6

7

6.1

16

13.9

115 (100.0)

Land-based and online

208

46.7

124

27.9

56

12.6

57

12.8

445 (100.0)

Gambling activities

Lottery

1059

60.7

323

18.5

219

12.6

143

8.2

1744 (100.0)

Instant scratch-off tickets**

853

60.6

276

19.6

181

12.9

98

7.0

1408 (100.0)

Bingo***

212

51.0

92

22.1

55

13.2

57

13.7

416 (100.0)

Sports betting***

139

43.3

98

30.5

42

13.1

42

13.1

321 (100.0)

Horse race track betting***

201

61.9

75

23.1

18

5.5

31

9.5

325 (100.0)

Live poker***

129

51.2

70

27.8

26

10.3

27

10.7

252 (100.0)

Live casino table games***

264

57.0

104

22.5

42

9.1

53

11.4

463 (100.0)

Gaming machines (slots)

416

60.4

139

20.2

73

10.6

61

8.8

689 (100.0)

Other games of skill***

158

45.7

99

28.6

47

13.6

42

12.1

346 (100.0)

Substance use

Tobacco use***

351

53.6

156

23.8

93

14.2

55

8.4

655 (100.0)

Alcohol use***

1051

62.2

325

19.2

178

10.5

136

8.0

1690 (100.0)

Binge drinking***

230

51.7

120

27.0

45

10.1

50

11.2

445 (100.0)

Illegal drug use***

116

44.3

82

31.3

41

15.6

23

8.8

262 (100.0)

Problems with drugs or alcohol***

44

40.4

43

39.4

13

11.9

9

8.9

109 (100.0)

Mental health

Behavioral addictions*

165

55.9

74

25.1

35

11.9

21

7.1

295 (100.0)

Mental health problems*

183

60.4

70

23.1

35

11.6

15

5.0

303 (100.0)

Suicidal ideation***

33

44.0

26

34.7

11

14.7

5

6.7

75 (100.0)

* p ≤ .05; ** p ≤ .01; *** p ≤ .001

Multivariate analyses

A primary aim of this study was to identify the primarily predictors of those at moderate or high risk for gambling problems (i.e. 3+ symptoms) compared to non-problem gamblers (i.e. zero symptoms). For that reason, medium and high risk participants were recoded as “problem gamblers” and compared to non-problem gamblers. Low risk gamblers were omitted from the analyses to ensure comparisons between those with more serious symptoms to those with an absence of symptoms. Multiple logistic regression analyses were used to evaluate the relative contributions of the predictor variables, which had proven significant in the univariate analyses, to the likelihood of membership in the at-risk problem gambling group. Continuous variables included age and number of gambling activities endorsed for the past year. All other variables were dummy coded. The minimum criteria for entry of covariates into the model were a p value of less than .05. Partial odds ratios (OR) and 95% confidence intervals (CIs) were computed for significant predictors. Model effects were estimated by the improvement in Chi-square and by a classification matrix indicating the proportion of individuals correctly identified by the model covariates.

To facilitate the identification of specific demographic, mental health, gambling participation, and substance use characteristics that differentiate non-problem gamblers from problem gamblers in Whites and ethnic minorities, backward selection step-wise logistic regression analyses were performed, entering in Block 1 demographic variables that had proven significant in the prior analyses between the two groups. These included gender, age, marital status, whether friends or family gamble, overall health in the past year, and overall stress levels in the past year. Substance use, behavioral addiction, and mental health variables were entered in Block 2, to determine which of the significant variables added most to the regression equation overall and which, if any, had a moderating effect on the significant demographic characteristics. Gambling behavior variables were entered into Block 3 to similarly determine which added the most to the regression equation overall and had a moderating effect on the remaining Block 1 and Block 2 variables. Tables 3 and 4 show the final regression results.
Table 3

Variables distinguishing between White non-problem gamblers (n = 1016) and White at-risk gamblers (n = 325)

 

SE

OR

95% CI

Age (continuous)***

0.01

0.98

0.97–0.99

Gender (female)*

0.17

1.44

1.03–2.02

Friends and family gamble***

0.17

2.28

1.64–3.18

Health status for the last year

Excellent (ref.)

Fair**

0.31

2.69

1.46–4.94

Poor*

0.25

1.64

1.00–2.69

Tobacco use**

0.18

1.73

1.22–2.44

Alcohol use

0.21

0.20

0.50–1.15

Binge drinking

0.21

1.50

0.99–2.26

Problems with drugs or alcohol*

0.51

2.77

1.03–7.47

Behavioral addictions**

0.23

1.84

1.16–2.91

Gambling frequency

Low (ref.)

Medium*

0.23

1.70

1.08–2.68

High***

0.22

2.80

1.83–4.29

Gambling venue

Land-based only (ref.)

Online and land-based***

0.23

2.74

1.76–4.26

Online only**

0.33

2.55

1.35–4.81

Instant scratch-off***

0.20

2.72

1.83–4.04

Sports betting**

0.28

2.35

1.36–4.05

Horse race track

0.25

.66

0.40–1.08

Live casino table games*

0.21

1.65

1.10–2.47

Other games of skill*

0.24

1.75

1.09–2.81

Gaming machines*

0.18

1.59

1.12–2.27

p ≤ .05; ** p ≤ .01; *** p ≤ .001

Table 4

Variables distinguishing between Minority non-problem gamblers (n = 494) and Minority at-risk problem gamblers (n = 338)

 

SE

OR

95% CI

Age (continuous)*

0.01

0.98

0.97–1.00

Gender (female)

0.20

0.68

0.74–1.60

Friends and family gamble***

0.19

2.95

2.04–4.26

Overall stress level in the past year

Low (ref.)

Moderate

0.24

1.29

0.81–2.05

High

0.39

1.08

0.50–2.31

Relationship status

Married (ref.)

Divorced, separated, or widowed

0.31

1.02

0.56–1.88

Single

0.22

0.86

0.56–1.32

Tobacco use

0.21

1.42

0.96–2.16

Binge drinking

0.22

1.33

0.87–2.03

Illegal drug use

0.27

1.58

0.90–2.59

Behavioral addictions**

0.28

2.16

1.26–3.86

Suicidal ideation in the past year

0.61

1.61

0.46–5.20

Gambling frequency low (ref.)

Medium***

0.28

3.60

2.08–6.24

High***

0.27

4.53

2.67–7.70

Gambling venue

Land-based only (ref.)

Online and land-based

0.24

1.53

0.96–2.44

Online only*

0.38

2.47

1.17–5.21

Instant scratch-off*

0.22

1.63

1.06–2.50

Bingo

0.25

1.54

0.95–2.49

Sports betting

0.28

1.63

0.95–2.81

Live casino table games

0.26

1.56

0.95–2.58

Gaming machines*

0.22

1.55

1.02–2.36

p ≤ .05; ** p ≤ .01; *** p ≤ .001

The results of both logistic regressions indicated a good model fit. The regression model separating White non-problem gamblers and at-risk problem gamblers presented with a Hosmer–Lemeshow goodness-of-fit statistic of, χ2 (8, N = 1341) = 2.91, p = .940. The second regression model separating ethnic minority non-problem gamblers and at-risk problem gamblers presented with a Hosmer–Lemeshow goodness-of-fit statistic of, χ2 (8, N = 832) = 10.25, p = .248. The largest predictors for membership in the White at-risk problem gambler group in the final model were high frequency gambling, having problems with drugs or alcohol, gambling both online and in land-based venues, and participating in instant scratch-off tickets. The largest predictors for membership in the minority at-risk problem gamblers group in the final model were high and moderate frequency gambling, having friends or family that gamble, and gambling online only.

Among Whites, the results indicate a significant negative relationship with age: Each one-year increase in age decreased the odds of being an at-risk problem gambler by .98%. Men were 1.44 times more likely to be White at-risk problem gamblers in comparison to women. Having friends or family who gambled increased the odds of being a White at-risk problem gambler by 2.28 times. Whites were also characterized by fair (2.69 times) or poor (1.64 times) health status in the past year, using tobacco products (1.73 times), having problems with drugs or alcohol (2.77 times) and/or a behavioral addiction (1.84 times).

Among Whites, high frequency (2.8 times) or moderate frequency (1.7 times) gambling, gambling online (2.6 times) or both online and in land-based venues (2.7 times), purchasing scratch-off tickets (2.7 times), betting on sports (2.3 times), playing games of skill (1.8 times), live casino games (1.7 times) and/or gaming machines (1.6 times) were most predictive of at-risk problem gamblers.

Among ethnic minorities, there was a similar negative relationship with age: Each one-year increase decreased the odds of being an at-risk problem gambler. Gender was a non-significant predictor for minorities, although having friends or family that gambled proved the most significant predictor for minority at-risk problem gambling status, increasing the odds by nearly three times. Among the substance use and mental health variables, only having a behavioral addiction was significant predictor of at-risk problem minority membership, increasing the odds by 2.0 times. As with Whites, moderate or high frequency gambling increased the odds of being an at-risk problem gambler by 3.6 and 4.5 times, respectively. Unlike Whites, however, gambling both online and in land-based venues was not a significant predictor of being at-risk, although gambling only online increased the odds of membership by 2.5 times. Amongst the individual gambling activities, only instant scratch-off tickets and gaming machine participation were predictive of at-risk minority status (2.72 and 1.59 times respectively).

Discussion

Findings from this study highlight the need to further explore ethnic differences among gamblers and to better differentiate etiological and other risk factors that may variously predispose different ethnic groups to develop gambling problems. The study utilized a representative sample of participants from New Jersey, however, the relatively small sample size of each ethnic sub-group compared to Whites precluded a detailed exploration of differences within each sub-group in the multivariate analyses. The data suggested that, overall, Whites were more likely than other ethnic groups to be non-problem gamblers; they were also more likely than other ethnic groups, irrespective of problem gambling severity, to be younger males from families or peer groups that gambled and to report comorbid addictive behaviors and fair to poor health status. This profile reflects the characterization of the “emotionally vulnerable” problem gambler (Blaszczynski and Nower 2002), who gambles problematically in order to escape aversive mood states and develops problems due to gambling with increasing frequency on multiple gambling games. Like Whites, Ethnic minority groups appear to be primarily influenced by family members or peer groups who gambled, however, unlike Whites, gender did not appear to play a predictive role. As with Whites, higher gambling frequency among minorities was correlated with higher levels of problem severity, although gambling only online and presumably on gaming machines appeared to be a greater risk factor. These findings could suggest that the influence of cultural, familial and community attitudes about gambling, combined with accessibility of opportunities and the conditioning effects of reinforcement could lead to gambling problems in some minority subgroups. This etiology, characteristic of “behaviorally conditioned” problem gamblers (Blaszczynski and Nower 2002), is most responsive to targeted prevention, interventions, and education efforts directed at the client system.

In contrast to findings in an earlier study (Alegria et al. 2009), the current results fail to support the notion of a “Hispanic paradox” for gambling and suggest a far more complex and context-dependent array of risk factors likely play a role. In this study, Hispanics were distinguished by the highest rates of problem gambling, substance abuse, and mental health problems. Though Asian participants also endorsed high rates of problem gambling, Hispanic gamblers reported the highest proportionate rates of “action” oriented play, such as sports and race track betting and casino table games, and gambling primarily online. They were also more likely than other ethnic groups to endorse substance abuse, mental health problems and suicidality in the past year.

Very little is known about the onset of gambling and problem gambling in Hispanic communities, the influence of peers and family modeling, the role of erroneous cognitions generated by cultural superstitions, and/or other bio-psycho-social factors that lead to the development and maintenance of gambling problems in sub-groups of Hispanics and Latinos. In New Jersey, Hispanics are the largest minority but their median income is almost half that of Whites and less than half that of Asians (U.S. Census Bureau 2015), however, there are few programs and services targeting Hispanic gamblers and few certified gambling counselors who are Spanish-speakers. Future research with Hispanics and other ethnic minorities should focus on exploring the cultural and familial systems that introduce and help to maintain gambling behavior in various ethnic groups and investigating specific risk and protective factors to use as a basis for prevention, intervention and treatment efforts.

Declarations

Authors’ contributions

All authors participated on the development of this manuscript. All authors read and approved the final manuscript.

Acknowledgements

The researchers would like to thank Director David L. Rebuck, Robert Moncrief, and Afshien Lashkari of the DGE, Suzanne Borys from DMHAS, Dr. Rachel Volberg of Gemini Research, and Simon Jaworski and Lance Henik of Leger for their assistance with this project.

Competing interests

Funding was provided to the DGE by law by industry corporations with online gaming licenses in New Jersey. Authors Caler and Vargas Garcia are students, employed through that grant. Dr. Nower has received grants from or consulting contracts from industry, governmental, and/or non-profit organizations on projects unconnected to this work. All authors certify they have no competing interests regarding this study or its findings.

Availability of data and materials

The data is proprietary and not publically available.

Consent to publication

All authors consent to publication of this manuscript.

Ethics approval

All procedures performed in studies involving human participants were approved by the Rutgers University Internal Review Board and performed in accordance with their ethical standards and those of the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Funding

This study was supported by a grant from the New Jersey Divisions of Gaming Enforcement (DGE), in collaboration with the Division on Addictions, Department of Mental Health and Addictive Services (DMHAS).

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Authors’ Affiliations

(1)
Center for Gambling Studies, School of Social Work, Rutgers University

References

  1. Alegria, A. A., Petry, N. M., Hasin, D. S., Liu, S. M., Grant, B. F., & Blanco, C. (2009). Disordered gambling among racial and ethnic groups in the US: Results from the national epidemiologic survey on alcohol and related conditions. CNS Spectrums, 14(3), 132–142.View ArticleGoogle Scholar
  2. Barnes, G. M., Welte, J. W., Hoffman, J. H., & Tidwell, M. O. (2009). Gambling, alcohol, and other substance use among youth in the United States. Journal of Studies on Alcohol and Drugs, 70(1), 134–142.View ArticleGoogle Scholar
  3. Barry, D. T., Stefanovics, E. A., Desai, R. A., & Potenza, M. N. (2011a). Gambling problem severity and psychiatric disorders among Hispanic and white adults: Findings from a nationally representative sample. Journal of Psychiatric Research, 45(3), 404–411.View ArticleGoogle Scholar
  4. Barry, D. T., Stefanovics, E. A., Desai, R. A., & Potenza, M. N. (2011b). Differences in the associations between gambling problem severity and psychiatric disorders among Black and White adults: Findings from the national epidemiologic survey on alcohol and related conditions. American Journal on Addictions, 20(1), 69–77.View ArticleGoogle Scholar
  5. Barry, D. T., Steinberg, M. A., Wu, R., & Potenza, M. N. (2008). Characteristics of black and white callers to a gambling helpline. Psychiatric Services, 59(11), 1347–1350.View ArticleGoogle Scholar
  6. Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 97(5), 487–499.View ArticleGoogle Scholar
  7. Canale, N., Vieno, A., Griffiths, M. D., Borraccino, A., Lazzeri, G., Charrier, L., et al. (2017). A large-scale national study of gambling severity among immigrant and non-immigrant adolescents: The role of the family. Addictive Behaviors, 66, 125–131.View ArticleGoogle Scholar
  8. Chamberlain, S. R., Leppink, E., Redden, S. A., Odlaug, B. L., & Grant, J. E. (2016). Racial-ethnic related clinical and neurocognitive differences in adults with gambling disorder. Psychiatry Research, 242, 82–87.View ArticleGoogle Scholar
  9. Clark, R., King, B., & Laylim, D. (1990). Tin Sin Kuk (Heavenly Swindle). New South Wales: Asian Community Research Unit State Intelligence Group.Google Scholar
  10. Dhillon, J., Horch, J. D., & Hodgins, D. C. (2011). Cultural influences on stigmatization of problem gambling: East Asian and Caucasian Canadians. Journal of Gambling Studies, 27(4), 633–647.View ArticleGoogle Scholar
  11. Ferris, J., & Wynne, H. (2001). The Canadian problem gambling index. Ottawa, ON: Canadian Centre on Substance Abuse.Google Scholar
  12. Grant, B. F., Hasin, D. S., Stinson, F. S., Dawson, D. A., Chou, S. P., Ruan, W., et al. (2004). Prevalence, correlates, and disability of personality disorders in the United States: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Journal of Clinical Psychiatry, 65(7), 948–958.View ArticleGoogle Scholar
  13. Hardoon, K. K., Baboushkin, H. R., Derevensky, J. L., & Gupta, R. (2001). Underlying cognitions in the selection of lottery tickets. Journal of Clinical Psychology, 57(6), 749–763.View ArticleGoogle Scholar
  14. Kessler, R. C., Hwang, I., LaBrie, R., Petukhova, M., Sampson, N. A., Winters, K. C., et al. (2008). DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychological Medicine, 38(9), 1351–1360.View ArticleGoogle Scholar
  15. Luczak, S. E., & Wall, T. L. (2016). Gambling problems and comorbidity with alcohol use disorders in Chinese-, Korean-, and White-American college students. The American Journal on Addictions, 25(3), 195–202.View ArticleGoogle Scholar
  16. Marshall, G. N., Elliott, M. N., & Schell, T. L. (2009). Prevalence and correlates of lifetime disordered gambling in Cambodian refugees residing in Long Beach, CA. Journal of Immigrant and Minority Health, 11(1), 35–40.View ArticleGoogle Scholar
  17. Momper, S. L., Nandi, V., Ompad, D. C., Delva, J., & Galea, S. (2009). The prevalence and types of gambling among undocumented Mexican immigrants in New York City. Journal of Gambling Studies, 25(1), 49–65.View ArticleGoogle Scholar
  18. Ngai, M., & Chu, K. (2001). Issues and service needs of Chinese communities. Social Work Review, 13(1), 2–6.Google Scholar
  19. Nower, L., & Blaszczynski, A. (2006). Characteristics and gender differences among self-excluded casino problem gamblers: Missouri data. Journal of Gambling Studies, 22(1), 81–99.View ArticleGoogle Scholar
  20. Nower, L., Eyrich-Garg, K. M., Pollio, D. E., & North, C. S. (2015). Problem gambling and homelessness: Results from an epidemiologic study. Journal of Gambling Studies, 31(2), 533–545.View ArticleGoogle Scholar
  21. Petry, N. M., Armentano, C., Kuoch, T., Norinth, T., & Smith, L. (2003). Gambling participation and problems among South East Asian refugees to the United States. Psychiatric Services, 54(8), 1142–1148.View ArticleGoogle Scholar
  22. Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: Results from the national epidemiologic survey on alcohol and related conditions. Journal of Clinical Psychiatry, 66(5), 564–574.View ArticleGoogle Scholar
  23. Raylu, N., & Oei, T. P. (2004). Role of culture in gambling and problem gambling. Clinical Psychology Review, 23(8), 1087–1114.View ArticleGoogle Scholar
  24. Sacco, P., Torres, L. R., Cunningham-Williams, R. M., Woods, C., & Unick, G. J. (2011). Differential item functioning of pathological gambling criteria: An examination of gender, race/ethnicity, and age. Journal of Gambling Studies, 27(2), 317–330.View ArticleGoogle Scholar
  25. Scribner, R. (1996). Paradox as paradigm—The health outcomes of Mexican Americans. American Journal of Public Health, 86(3), 303–305.View ArticleGoogle Scholar
  26. Stefanovics, E. A., Potenza, M. N., & Pietrzak, R. H. (2017). Gambling in a National U.S. Veteran Population: Prevalence, socio-demographics, and psychiatric comorbidities. Journal of Gambling Studies. doi:10.1007/s10899-017-9678-2.Google Scholar
  27. Toyama, T., Nakayama, H., Takimura, T., Yoshimura, A., Maesato, H., Matsushita, S., et al. (2014). Prevalence of pathological gambling in Japan: Results of national surveys of the general adult population in 2008 and 2013. Alcohol and Alcoholism, 49(suppl 1), i1–i69.View ArticleGoogle Scholar
  28. Tse, S. (2003, September). From claiming to changing: An Asian perspective in Aotearoa. In 3rd International conference on gambling through a public health lens: Health promotion, harm minimization and treatment.Google Scholar
  29. United States Census Bureau. (2015). “Summary file.” American Community Survey. U.S. Census Bureau’s American Community Survey Office, 2017. Web. 1 May 2017. https://www.census.gov/programs-surveys/acs/data/summary-file.2015.html.
  30. Vega, W. A., Alderete, E., Kolody, B., & Aguilar-Gaxiola, S. (1998). Illicit drug use among Mexicans and Mexican Americans in California: The effects of gender and acculturation. Addiction, 93(12), 1839–1850.View ArticleGoogle Scholar
  31. Volberg, R. A., & Abbott, M. W. (1997). Gambling and problem gambling among indigenous peoples. Substance Use and Misuse, 32(11), 1525–1538.View ArticleGoogle Scholar
  32. Welte, J. W., Barnes, G. M., Tidwell, M. O., & Hoffman, J. H. (2008). The prevalence of problem gambling among US adolescents and young adults: Results from a national survey. Journal of Gambling Studies, 24(2), 119–133.View ArticleGoogle Scholar
  33. Welte, J. W., Barnes, G. M., Tidwell, M.-C. O., & Wieczorek, W. F. (2017). Predictors of problem gambling in the U.S. Journal of Gambling Studies, 33(2), 327–342.View ArticleGoogle Scholar
  34. Welte, J., Barnes, G., Wieczorek, W., Tidwell, M. C., & Parker, J. (2001). Alcohol and gambling pathology among US adults: Prevalence, demographic patterns and comorbidity. Journal of Studies on Alcohol, 62(5), 706–712.View ArticleGoogle Scholar
  35. Westermeyer, J., Canive, J., Garrard, J., Thuras, P., & Thompson, J. (2005). Lifetime prevalence of pathological gambling among American Indian and Hispanic American veterans. American Journal of Public Health, 95(5), 860–866.View ArticleGoogle Scholar
  36. Wilson, A. N., Salas-Wright, C. P., Vaughn, M. G., & Maynard, B. R. (2015). Gambling prevalence rates among immigrants: A multigenerational examination. Addictive Behaviors, 42, 79–85.View ArticleGoogle Scholar
  37. Zitzow, D. (1996a). Comparative study of problematic gambling behaviors between American Indian and non-Indian adolescents within and near a northern plains reservation. American Indian and Alaska Native Mental Health Research, 7, 14–26.View ArticleGoogle Scholar
  38. Zitzow, D. (1996b). Comparative study of problematic gambling behaviors between American Indian and non-Indian adults in a northern plains reservation. American Indian and Alaska Native Mental Health Research, 7, 27–41.View ArticleGoogle Scholar

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