Problem gambling among ethnic minorities: results from an epidemiological study

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.

Full text

Problem gambling amongethnic
minorities: results froman epidemiological
study
Kyle R. Caler, Jose Ricardo Vargas Garcia and Lia Nower*
Background
Studies have consistently reported high rates of problem gambling among racial and eth-
nic 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 prob-
lem 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
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 pre-
sent 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 signifi-
cantly 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, sub-
stance 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.
Open Access
© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.
RESEARCH ARTICLE
Caler et al.
Asian J of Gambling Issues and Public Health (2017) 7:7
DOI 10.1186/s40405-017-0027-2
*Correspondence:
lnower@rutgers.edu
Center for Gambling
Studies, School of Social
Work, Rutgers University,
536 George Street, New
Brunswick, NJ 08901, USA

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
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). ese find-
ings 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).
ere is scant research involving Hispanics/Latinos and gambling. e 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. e 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 tick-
ets or the lottery (Momper etal. 2009). ose 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, Chi-
nese students reported the highest rates of gambling problems followed by Koreans then
Whites. e 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).
e 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

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
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 adoles-
cents 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 biol-
ogy, 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 compulsiv-
ity, 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 clin-
ically 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 immi-
grant 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). is phenomenon seems to parallel the “Hispanic para-
dox” (Scribner 1996) documented in health outcome studies, where Hispanics have bet-
ter 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
e 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 partici-
pants. e remaining 1461 participants reported no involvement in any gambling activi-
ties in the past year and were excluded from the analyses. Data coding and analyses were
conducted using SPSS version 24.

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
Measures
e present study incorporated data collected through an epidemiological survey con-
ducted 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/ethnic-
ity, education level, household income, immigration status, and relationship status); (b)
substance use (tobacco use, alcohol use, illegal drug use, problems and treatment seek-
ing 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 partici-
pated 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) is 9-item instrument was used to assess gambling sta-
tus. 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 moder-
ate 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 analy-
ses, 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
e 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/eth-
nicity 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 vari-
ables by PGSI category. e association between the PGSI and each explanatory vari-
able was assessed using Chi-squared Test of Independence. No socioeconomic variables
showed a significant association with the PGSI. High risk of problem gambling was sig-
nificantly 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).

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
Table 1 Demographic breakdown ofnon-problem (n = 1510) and at-risk problem gam-
blers (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)

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
Additionally, Table2 presents associations between race/ethnicity and gambling fre-
quency, preferred gambling venue(s), participation in individual gambling activities, five
measures of substance use, and three measures of mental health. Race/ethnicity was sig-
nificantly 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 asso-
ciated 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 pre-
ferred 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
Table 1 continued
Variable Non-PG Low risk PG Moderate
risk PG High risk PG Total
n%n%n%n%n (% oftotal)
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

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
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.
Multivariate analyses
A primary aim of this study was to identify the primarily predictors of those at mod-
erate or high risk for gambling problems (i.e. 3+ symptoms) compared to non-prob-
lem 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
Table 2 Gambling, substance use, andmental health byethnicity
*p≤.05; **p≤.01; ***p≤.001
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 alco-
hol*** 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)

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
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. e 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 par-
ticipation, 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. ese 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 moder-
ating 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 vari-
ables. Tables3 and 4 show the final regression results.
e results of both logistic regressions indicated a good model fit. e 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.
e 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. e 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. e largest predictors for membership in the
minority at-risk problem gamblers group in the final model were high and moderate fre-
quency 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) gam-
bling, 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.

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
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 gam-
bled 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 predic-
tive 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. e study utilized a
Table 3 Variables distinguishing between White non-problem gamblers (n = 1016)
andWhite at-risk gamblers (n=325)
*p≤.05; **p≤.01; ***p≤.001
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

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
representative sample of participants from New Jersey, however, the relatively small sam-
ple size of each ethnic sub-group compared to Whites precluded a detailed exploration
of differences within each sub-group in the multivariate analyses. e data suggested
that, overall, Whites were more likely than other ethnic groups to be non-problem gam-
blers; they were also more likely than other ethnic groups, irrespective of problem gam-
bling severity, to be younger males from families or peer groups that gambled and to
report comorbid addictive behaviors and fair to poor health status. is 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. ese find-
ings could suggest that the influence of cultural, familial and community attitudes about
Table 4 Variables distinguishing between Minority non-problem gamblers (n = 494)
andMinority at-risk problem gamblers (n=338)
*p≤.05; **p≤.01; ***p≤.001
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

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Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
gambling, combined with accessibility of opportunities and the conditioning effects of
reinforcement could lead to gambling problems in some minority subgroups. is eti-
ology, 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 com-
plex and context-dependent array of risk factors likely play a role. In this study, Hispan-
ics were distinguished by the highest rates of problem gambling, substance abuse, and
mental health problems. ough Asian participants also endorsed high rates of problem
gambling, Hispanic gamblers reported the highest proportionate rates of “action” ori-
ented play, such as sports and race track betting and casino table games, and gambling
primarily online. ey were also more likely than other ethnic groups to endorse sub-
stance 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 cogni-
tions 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 His-
panics 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 gam-
blers 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 preven-
tion, intervention and treatment efforts.
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 consult-
ing 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).

Page 12 of 13
Caler et al. Asian J of Gambling Issues and Public Health (2017) 7:7
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Received: 26 May 2017 Accepted: 13 August 2017
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