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Problem gambling and substance use in patients attending community mental
health services
VICTORIA MANNING
1,2
*, NICKI. A. DOWLING
3,4
, STUART LEE
5
, SIMONE RODDA
1,3,6
,
JOSHUA BENJAMIN BERNARD GARFIELD
1,2
, RACHEL VOLBERG
7
, JAYASHRI KULKARNI
5
and DAN IAN LUBMAN
1,2
1
Turning Point, Eastern Health, Melbourne, VIC, Australia
2
Eastern Health Clinical School, Monash University, Melbourne, VIC, Australia
3
School of Psychology, Deakin University, Geelong, VIC, Australia
4
Melbourne Graduate School of Education, University of Melbourne, Melbourne, VIC, Australia
5
Monash Alfred Psychiatry Research Centre, Alfred Health and Monash University Central Clinical School, Melbourne, VIC, Australia
6
School of Population Health, University of Auckland, Auckland, New Zealand
7
School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
(Received: July 20, 2017; revised manuscript received: October 29, 2017; accepted: November 19, 2017)
Background and aims: Relatively little is known about co-occurring gambling problems and their overlap with other
addictive behaviors among individuals attending mental health services. We aimed to determine rates of gambling
and substance use problems in patients accessing mental health services in Victoria, Australia. Methods: A total of
837 adult patients were surveyed about their gambling and administered standardized screening tools for problem
gambling and harmful tobacco, alcohol, and drug use. Prevalence of gambling problems was estimated and regression
models used to determine predictors of problem gambling. Results: The gambling participation rate was 41.6%
[95% CI =38.2–44.9]. The Problem Gambling Severity Index identified 19.7% [CI =17.0–22.4] as “non-problem
gamblers,”7.2% [CI =5.4–8.9] as “low-risk”gamblers, 8.4% [CI =6.5–10.2] as “moderate-risk”gamblers, and
6.3% [CI =4.7–8.0] as “problem gamblers.”One-fifth (21.9%) of the sample and 52.6% of all gamblers were
identified as either low-risk, moderate-risk, or problem gamblers (PGs). Patients classified as problem and moderate-
risk gamblers had significantly elevated rates of nicotine and illicit drug dependence (p<.001) according to short
screening tools. Current diagnosis of drug use (OR =4.31 [CI =1.98–9.37]), borderline personality (OR =2.59
[CI =1.13–5.94]), bipolar affective (OR =2.01 [CI =1.07–3.80]), and psychotic (OR =1.83 [CI =1.03–3.25])
disorders were significant predictors of problem gambling. Discussion and conclusions: Patients were less likely
to gamble, but eight times as likely to be classified as PG, relative to Victoria’s adult general population. Elevated
rates of harmful substance use among moderate-risk and PG suggest overlapping vulnerability to addictive behaviors.
These findings suggest mental health services should embed routine screening into clinical practice, and train
clinicians in the management of problem gambling.
Keywords: problem gambling, mental health, alcohol, nicotine, illicit drugs
INTRODUCTION
While gambling is a popular pastime for many individuals, it
remains a significant public health issue in Australia, with
adverse impacts on psychological, social, familial, and/or
occupational functioning (Jauregui, Urbiola, & Estevez,
2016;Langham et al., 2016;Li, Browne, Rawat, Langham,
& Rockloff, 2017). Although the latest edition of the Diag-
nostic and Statistical Manual of Mental Disorders (DSM-5;
American Psychiatric Association, 2013) reclassified “path-
ological gambling”as “Gambling Disorder”under “addiction
and related disorders,”gambling problems are often concep-
tualized across a risk continuum. In Victoria, Australia, a
recent household survey found that 70.1% of adults had
gambled in the past year, with 0.8% identified as problem
gamblers (PGs), 2.8% as moderate-risk gamblers, 8.9% as
low-risk gamblers, and 57.6% as non-PGs (Hare, 2015).
Systematic reviews of epidemiological research, predom-
inantly from the USA, have consistently revealed high rates
of comorbidity between gambling and mental health dis-
orders. These studies reveal a high prevalence of mental
health conditions among problem and/or pathological gam-
blers in general population samples (57.5% comorbid sub-
stance use disorder and 57% comorbid mood or anxiety
disorder) (Lorains, Cowlishaw, & Thomas, 2011). Similar-
ly, among those who are seeking treatment for gambling
problems up to three quarters have a comorbid DSM-IV
Axis I disorder, most commonly mood disorder (23.1%)
and/or any substance use disorder (22.2%) (Dowling et al.,
* Corresponding author: Victoria Manning; Turning Point, Eastern
Health, 110 Church Street, Richmond 3121, VIC, Australia; Phone:
+61 3 8413 8413; Fax: +61 3 9416 3420; E-mail: victoriam@
turningpoint.org.au
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited.
© 2017 The Author(s)
FULL-LENGTH REPORT Journal of Behavioral Addictions
DOI: 10.1556/2006.6.2017.077
2015b); and almost half have co-occurring personality dis-
orders (Dowling et al., 2015a). There is also systematic review
evidence that early alcohol use frequency, cannabis use, illicit
drug use, tobacco use, and depressive symptoms, but not
anxiety symptoms, are longitudinally associated with the
development of gambling problems, with small but significant
effect sizes (Dowling et al., 2017). Finally, there is also limited
evidence from a growing number of studies that problem
gambling is a risk factor for the subsequent occurrence of
some mental health disorders, including mood disorders,
anxiety disorders, and alcohol and other drug (AOD) use
disorders (Chou & Afifi, 2011;Parhami, Mojtabai, Rosenthal,
Afifi,&Fong,2014;Pilver, Libby, Hoff, & Potenza, 2013).
The high prevalence of comorbid substance use disorders
among mental health treatment seekers (with estimates of
25%–65%) (Croton, 2005;Hartz et al., 2014;Manning et al.,
2008;Zimmermann, Lubman, & Cox, 2012)iswell-
established in the literature. However, few studies have
examined the prevalence of gambling problems among this
population, particularly in Australia. There is, however,
systematic review and meta-analytic evidence from the
international literature that gambling problems are overrep-
resented in AOD services (10.0%–43.4%) (Cowlishaw,
Merkouris, Chapman, & Radermacher, 2014). A much more
limited literature is also emerging internationally to suggest
that problem gambling is also overrepresented in psychiatric
outpatient services that treat patients with a range of
psychiatric disorders (2.0%–4.4%) (Dowling et al., 2014;
Henderson, 2004;Nehlin, Gronbladh, Fredriksson, & Jansson,
2013;Zimmerman, Chelminski, & Young, 2006a,2006b).
In Australia, however, only three studies have examined the
prevalence of problem gambling in mental health treatment
populations using standardized sceening tools. Two of these
studies have explored the prevalence of PG in specificdis-
orders. Haydock, Cowlishaw, Harvey, and Castle (2015) iden-
tified a PG prevalence of 5.8% in outpatients with psychotic
disorders using the Problem Gambling Severity Index (PGSI),
whereas Biddle, Hawthorne, Forbes, and Coman (2005) iden-
tified a pathological gambling prevalence of 29.1% in veterans
with post-traumatic stress disorder using the South Oaks
Gambling Screen. In the only study to explore the prevalence
of problem gambling in Australian outpatient services that treat
patients with a variety of psychiatric disorders, Dowling et al.
(2014) found that 2% of the 51 individuals (i.e., one person)
met criteria for PG using a very brief screening instrument
(the Brief Bio-Social Gambling Screen).
To achieve a robust estimate of PG in Australian outpa-
tient services, there is a clear need to examine PG prevalence
across multiple service types and geographic locations
(i.e., metropolitan and regional areas), among patients with
a broad range of mental health disorders, and using gold
standard measurement of PG. Moreover, the earlier Austra-
lian studies failed to examine patterns of gambling activities,
nor interactions of PG with mental health and substance use
characteristics. Understanding PG and related behaviors is
critical for this clinical population, as they are often mar-
ginalized and stigmatized with high rates of unemployment
and less disposable income to finance gambling activities
(Haydock et al., 2015). Poor emotion/mood regulation and
impulsivity are common features of many psychiatric dis-
orders (Berking & Wupperman, 2012;Fox & Hammond,
2017) and may increase susceptibility to PG through
extended or less-controlled gambling episodes.
Problem gambling comorbid with mental health disorders
has been associated with increased psychiatric symptoms;
substance use problem severity; interpersonal, physical,
financial and social difficulties; impulsivity; and suicidality
(Cowlishaw, Hakes, & Dowling, 2016;Di Nicola et al., 2010;
Haydock et al., 2015;Jones et al., 2015;Kennedy et al., 2010),
thereby complicating clinical presentations and compromising
treatment engagement and effectiveness (Chou & Afifi, 2011).
Determining the prevalence of PG in this population is thus a
research priority. We therefore aimed to examine the preva-
lence of gambling problems across the risk continuum using a
standardized screening tool of PG severity, as well as its
relationship with harmful substance use and specificpsy-
chiatric diagnoses among patients attending a diverse range
of mental health services in Victoria, Australia, including, for
the first time, patients attending private mental health services.
METHODS
Participants
A total of 837 patients completed an anonymous online
survey assessing gambling, psychiatric diagnoses, and
substance use between June 2015 and January 2016. Parti-
cipants were recruited from eight separate outpatient mental
health services (across 12 individual sites, since some
service organizations operated more than one site) in
Victoria, Australia. To be representative of the Victorian
outpatient mental health treatment-seeking population, the
sample included patients from both public/state-funded and
private services, adult and youth community mental health
services, services in metropolitan and regional areas, and
included a state-wide community support service (MHCSS)
offering outreach, psychosocial rehabilitation, and support
(see Table 1for breakdown of service types). Patients were
eligible to participate if they were receiving treatment from
the mental health service and were aged over 18 years.
Clients were excluded if they were too acutely unwell to
participate or were unable to understand English. The
sample included 55% of the 1,528 patients attending those
services during the data collection periods. Of these, 165
patients were deemed by clinicians to be too unwell to
participate in the study, 855 agreed to participate, 4 were
excluded due to being aged under 18 years, and 14 withdrew
participation prior to completion of the survey.
Measures
The survey was developed in collaboration with clinicians and
consumers of mental health services who contributed to content
and language. The survey was extensively piloted in multiple
settings, underwent several revisions, with the final version
taking around 15 min to complete. The survey was hosted by
the online survey site Qualtrics, accessed on tablet computers
using a link, with a paper copy available for participants who
were unable or unwilling to use the online version. The survey
assessed demographic information (e.g., gender, ethnicity,
employment status, etc.) before proceeding to sections related
to gambling, psychiatric diagnoses, and substance use.
Journal of Behavioral Addictions
Manning et al.
Gambling. Participants were asked “Have you gambled at
any point/time in the last 12 months? Gambling includes
wagering on a race or event, buying a lottery ticket, playing
keno or playing cards at home –as well as playing the pokies
[electronic gaming machines (EGMs)] or betting on sports.”
Those who responded “yes”were asked to indicate the
frequency (per month) for different gambling activities (“In
the last 12 months, how many times per month have
you spent any money playing or betting on: [gambling
activity]”, asked for both venue based gambling and
gambling over the Internet) as well as past-month spending
on gambling activities (“In the last month how much money
in total did you spend on gambling?”). The severity of
gambling problems was assessed using the 9-item PGSI
(Ferris & Wynne, 2001), a standardized measure that iden-
tifies “non-PG”(a score of 0), “low-risk gambling”(a score of
1–2, indicating low-level problems with few or no identified
negative consequences), “moderate-risk gambling”(score of
3–7, indicating moderate-level problems leading to some
negative consequences), and “PG”(a score of 8–27, indicat-
ing PG with negative consequences and possible loss of
control), which was also used in the Victorian household
survey (Hare, 2015). Participants indicating that they had not
participated in any form of gambling in the past year (using
the above definition) skipped the entire section on gambling
behaviors. Participants were also asked if a mental health
clinician had ever asked them about their gambling at that
service.
Psychiatric diagnoses. Participants were presented with a
list of diagnoses including “depression,”“bipolar disorder
or mania,”“anxiety,”“psychotic disorder, such as schizo-
phrenia or schizoaffective disorder,”“eating disorder, such
as bulimia or anorexia,”“borderline personality disorder
(BPD),”“alcohol abuse or dependence,”“drug abuse or
dependence,”“gambling disorder,”and “other,”and asked
to indicate which disorders they had been diagnosed with
during their lifetime. If participants selected “anxiety,”they
were presented with an additional list of specific anxiety
disorders to select from. If participants selected “other,”they
were asked to type in additional diagnoses. Participants
were also asked to indicate which of the lifetime diagnoses
selected were current within the past year.
Substance use. The Alcohol Use Disorders Identifi-
cation Test –Consumption (AUDIT-C) (Bush, Kivlahan,
McDonell, Fihn, & Bradley, 1998) was used to assess
hazardous drinking (with a cut-off score of 3 for females
and 4 for males). Nicotine dependence was assessed using the
2-item Heavy Smoking Index (HSI; Heatherton, Kozlowski,
Frecker, Rickert, & Robinson, 1989). Illicit drug use was
assessed with the single-item Drug Use Screen (Smith,
Schmidt, Allensworth-Davies, & Saitz, 2010). If participants
indicated they had used illicit drugs or prescription medica-
tion for non-medical use in the past 12 months, they were
asked if they had a primary drug of concern (PDOC). If so,
level of dependence on their PDOC was assessed using the
Severity of Dependence Scale (SDS; Gossop et al., 1995),
where a score of 3+indicated probable dependence.
Procedure
With the exception of one service, researchers (n=10), all
with undergraduate psychology degrees and trained by the
project coordinator, were stationed in the waiting rooms
during clinic hours throughout the data collection period.
The time spent at each site was proportionate to the number
of clinicians and frequency of patient visits, but was gener-
ally 1–2 weeks. Researchers directly approached patients,
and explained that they were conducting a “survey about
gambling among people attending mental health services,”
emphasizing that they did not have to gamble to participate,
and that all responses were confidential and anonymous.
Patients who consented to participate then completed the
survey while waiting to see their clinician. The researcher
was available to assist participants in survey completion if
required (e.g., to clarify the meaning of questions). If a
participant was unable to use the tablet, the researcher either
entered the participant’s verbal responses into the tablet, in a
quiet secluded area/room away from the main waiting room
to maintain the patient’s confidentiality, or provided a paper
copy of the survey for the participant to complete. Com-
pleted paper copies of the survey were later entered into
Qualtrics by the researcher. In the MHCSS, support workers
Table 1. Characteristics of the sample
Characteristic Descriptive statistics
a
Gender (n=837)
Male 50.9 (426)
Female 48.3 (404)
Other 0.8 (7)
Age (n=825) 38 (13), 18–95
Remoteness area according to postcode (n=812)
Major city 94.2 (765)
Inner regional 5.7 (46)
Outer regional 0.1 (1)
Remote or very remote 0 (0)
Born in Australia (n=837) 77.8 (651)
Currently employed (n=837) 29.0 (243)
Fortnightly income (n=751)
Less than $800 53.1 (399)
$800–$1,599 34.2 (257)
$1,600–$2,599 8.4 (63)
$2,600 or more 4.3 (32)
Highest level of education (n=829)
Less than year 12 25.9 (215)
Year 12 only 25.6 (212)
TAFE, diploma, or apprenticeship 21.6 (179)
University degree 26.9 (223)
Relationship status (n=837)
Single and never married 64.8 (542)
Married or de facto 19.8 (166)
Separated or divorced 14.3 (120)
Widowed 1.1 (9)
Mental health service type (n=837)
Public adult 46.5 (389)
Public adolescent/youth 10.8 (90)
Private 36.0 (301)
Community support service 6.8 (57)
Note. Total sample n=837. Where statistics are based on smaller
numbers, this is due to missing data for some participants for some
variables.
a
Statistics are % (n) for all variables except age, where mean
(standard deviation) and range are presented.
Journal of Behavioral Addictions
Problem gambling in mental health service patients
were trained to administer the survey by members of the
research team and took tablets to home visits so that patients
could complete the survey in their home, with the support
worker available to assist if necessary. All participants were
offered a $10 supermarket gift card for their participation in
the study.
Statistical analyses
Analyses were conducted using IBM
®
SPSS
®
version 22.
All prevalence estimates for gambling participation,
low risk, moderate risk, and PG are reported with exact
binomial 95% confidence intervals. Using one-sample χ
2
tests, prevalence rates were compared with those in the
general population (Hare, 2015), where in a randomly
selected group of 837 Victorian adults, 6.78 PGs, 23.35
moderate-risk gamblers, and 74.58 low-risk gamblers were
expected. Statistical significance of differences in propor-
tions (e.g., across demographic variables) was examined
using Pearson’sχ
2
for categorical data. T-tests and analysis
of variance (ANOVA) tests were used to examine differ-
ences in continuous data between PGSI categories, with
Bonferroni pairwise post-hoc tests used to follow up signif-
icant ANOVA effects. Multinomial logistic regression was
used to determine whether specific mental health diagnoses
were predictors of PGSI category after controlling for gender,
with adjusted odds ratios and 95% confidence intervals
obtained. For the purpose of these analyses, non-gamblers
and non-PGs were combined into a single reference group,
against which the remaining PG categories were contrasted
(preliminary logistic regression analyses, controlling for gender
and restricted to non-gamblers and non-PGs, found that no
psychiatric diagnoses significantly differentiated non-PGs from
non-gamblers). Prevalence of substance use harm categories
(i.e., hazardous drinking, according to the AUDIT; nicotine
dependence according to the HSI; and illicit SDS score of 3+)
was compared between PG categories using Pearson’sχ
2
tests,
with pairwise Bonferroni post-hoc comparisons examined in
the case of statistically significant overall χ
2
test results.
Ethics
The study was approved by the Eastern Health Human
Research Ethics committee (EHHREC; reference number:
E02-2015). For sites not covered by the EHHREC, ethical
review was undertaken and approval granted by the Alfred
Hospital Ethics Committee (reference number: 245/15),
the Albury Wodonga Human Research Ethics Committee
(reference number: 411/15/6), and the Melbourne Health
Office of Research (reference number: 2015.158). Participants
were provided with information regarding the study on the
tablet computer used to administer the questionnaires and
asked to indicate agreement to participate before proceeding
to the questionnaires.
RESULTS
Demographic characteristics
Sample characteristics are shown in Table 1. Approximately
half (50.9%) of participants were male, 48.3% were female,
and 0.8% identified their gender as “other.”The mean age
was 38 (SD =13) years, 77.8% were Australian-born,
10.5% identified as belonging to an ethnic minority, and
2.0% identified as Aboriginal or Torres Strait Islander. In
addition, 64.8% were single or never married and 94.2%
were residing in a major city. With respect to employment
and income, 29.0% were in full-time- or part-time work,
43.7% received a disability support or other pension, and
53.1% had a personal fortnightly income of less than $800.
Around half of the sample (52.3%) had been attending the
mental health service from which they were recruited for
more than 1 year.
Mental health
Most participants (92.7%) reported having been given at
least one mental health diagnosis during their lifetime and
88.6% had a current mental health diagnosis in the past year
(current diagnosis). The most common current diagnoses
were major depression (54.7%), anxiety disorder (any;
48.3%), psychotic disorder (31.1%), and bipolar disorder
(17.3%). Table 2shows prevalence of all diagnoses.
Gambling participation
The gambling participation rate (any gambling in the past
year, excluding raffles, which were not assessed) was 41.6%
[95% CI =38.2–44.9] and was significantly higher among
male (n=198, 46.5%) than female participants [n=148,
36.6%; χ
2
(1, n=830) =8.27, p<.01]. As shown in Table 3,
the most common gambling activities were EGMs, then
lotteries, followed by horse/greyhound racing, and then
scratch tickets. Most gambling took place in venues,
although the most frequently played activity online was
betting on sports, horse or greyhound racing, and EGMs,
and betting on events exclusively took place online. Parti-
cipants who had gambled in the past month reported
Table 2. Lifetime and current mental health conditions self-
reported by participants
Psychiatric disorder
Lifetime Current
%n%n
Depression 64.3 538 54.7 458
Any anxiety disorder 53.8 450 48.3 404
Generalized anxiety disorder 41.3 346 36.6 306
Panic disorder, panic attacks, and
agoraphobia
22.5 188 17.6 147
Social anxiety 21.0 176 16.6 139
Post-traumatic stress disorder 15.7 131 12.5 105
Obsessive–compulsive disorder 8.6 72 5.7 48
Phobia 3.1 26 2.4 20
Psychotic disorder 34.4 288 31.1 260
Bipolar disorder 21.7 182 17.3 145
Drug abuse or dependence 12.8 107 6.8 57
Borderline personality disorder 11.7 98 8.5 71
Alcohol abuse or dependence 9.1 76 5.0 42
Eating disorder 7.9 66 3.5 29
Gambling disorder 2.0 17 0.7 6
Other disorders 3.5 29 3.3 28
Journal of Behavioral Addictions
Manning et al.
spending a mean of $176.73 (SD =$373.47), although this
was skewed by a small minority with very high gambling
expenditure –median past-month spent among past-month
gamblers was $50 (interquartile range =$20–$150).
PG severity
On the PGSI, the mean total score among the 348 partici-
pants who reported past-year gambling was 3.2 (SD =5.1,
range =0–27). Of the total sample, 165 (19.7% [95%
CI =17.0–22.4]) had a PGSI score in the non-PG range,
60 (7.2% [CI =5.4–8.9]) had a score in the low-risk range,
70 (8.4% [CI =6.5–10.2]) had a score in the moderate-risk
range, and 53 (6.3% [CI =4.7–8.0]) were identified as PGs.
The proportion of participants categorized as low risk,
moderate risk, or PG significantly differed from estimated
rates in the Victorian general population [χ
2
(3) =419.51,
p<.001]. Dichotomous contrasts comparing each category
with all others indicated that PG [χ
2
(1) =317.66, p<.001]
and moderate-risk gambling [χ
2
(1) =95.87, p<.001] were
more prevalent among participants, whereas low-risk gam-
bling was (non-significantly) less prevalent [χ
2
(1) =3.13,
p=.077] among participants than in the Victorian adult
general population. One in five participants (21.9%; 52.6%
of all gamblers) was identified as either low risk, moderate
risk, or PGs. Despite this, less than half (42.7%; n=357)
reported that they had been asked about their gambling since
attending the mental health service. ANOVA revealed
significant differences in mean past-month gambling expen-
diture across PG category [F(3, 338) =27.2, p<.001], with
post-hoc tests indicating that PGs (M=$439.79) spent
significantly more than each other category [11 times more
than non-PGs (M=$39.12); eight times more than low-risk
gamblers (M=$50.32); and three times more than moder-
ate-risk gamblers (M=$123.84)], although non-problem,
low-risk, and moderate-risk gamblers did not significantly
differ from each other.
Mental health diagnosis by PG severity
Multinomial logistic regression analysis was conducted
to examine associations between specific current psychiatric
diagnoses and PG category. These analyses were conducted
controlling for gender, since gambling prevalence signifi-
cantly differed by gender, and rates of some psychiatric
diagnoses are also known to differ by gender. As shown in
Table 4, psychotic disorder was the only diagnosis signifi-
cantly associated with both moderate-risk gambling and PG:
odds of moderate-risk gambling were more than doubled
and odds of PG almost doubled relative to non-problem
gambling in those with a current psychotic disorder. Parti-
cipants with a current drug use disorder diagnosis had a
more-than-fourfold increase in risk of being identified as a
PG, relative to those without a current drug-use disorder. PG
was also predicted by bipolar and BPD, both of which at
least doubled the odds of PG.
Substance use
Approximately half of participants (49.3%) reported smok-
ing tobacco in the past year, and these participants reported
spending a median of $60/week on tobacco. The mean HSI
score among smokers was 3.0 (SD =1.9), with 40.7% of
participants (82.6% of past-year smokers) identified as
nicotine-dependent, according to their HSI score. Two thirds
of participants (67.9%) had consumed alcohol in the past
year. Past-year drinkers reported spending a median of $15/
week on alcohol. More than one third (37.5%, or 55.3% of
those who consumed alcohol) were drinking at hazardous
levels according to the AUDIT-C. Just under a quarter
(24.3%) reported using an illicit drug or a prescription
medication for non-medical use in the past year, most
commonly cannabis (20.7%), amphetamines (13.0%), and
sedatives (9.1%). These participants reported spending a
median of $50/week on illicit drugs. Among the 113
participants who reported having a PDOC other than alco-
hol, the mean SDS score was 6.6 (SD =4.6) with 76.8%
(10.3% of all participants) indicating probable drug
dependence.
Substance use by PG severity
Rates of nicotine dependence (according to HSI score) and
illicit drug dependence (according to SDS score) signifi-
cantly differed by PG category (Table 5). Post-hoc pairwise
comparisons indicated that both moderate-risk and PGs had
higher rates of nicotine and drug dependence than non-
gamblers/non-PGs. For hazardous drinking (according to
AUDIT-C score), there was a near-significant trend for
differences across the PG categories, although in this in-
stance, it was low-risk gamblers that had the highest pro-
portion identified as hazardous drinkers.
DISCUSSION AND CONCLUSIONS
The study aimed to determine the prevalence of gambling
participation and of PG across the risk continuum among
patients attending a broad range of community-based mental
health services in Victoria, Australia. The overall rate of
gambling participation among the sample was 41.4%, sub-
stantially lower than the 61.6% [95% CI =59.1%–64.0%]
reported among the general adult population in Victoria
when excluding gambling on raffles, as our study did (Hare,
2015). As expected, gambling participation was more com-
mon among male than female participants, and EGMs and
Table 3. Proportion of the sample participating in each form of
gambling within the past year
Gambling type % (n)
Electronic gaming machines (i.e., “pokies”) 20.9 (175)
Lotteries, powerball, or pools 20.7 (173)
Horse or greyhound racing 10.9 (91)
Scratch tickets 10.5 (88)
Casino table games 5.7 (48)
Sports betting 5.0 (42)
Keno 3.0 (25)
Informal private betting 2.5 (21)
Bingo 1.4 (12)
Betting on other events 0.6 (5)
Journal of Behavioral Addictions
Problem gambling in mental health service patients
lotteries were the most common activities, echoing
the findings of earlier research on community samples
(Bonnaire et al., 2016;Castrén et al., 2013).
Despite the lower rates of gambling participation, relative
to the Victorian adult population, participants were more
likely to be categorized within the PG risk categories, with
one in five participants (half of those who gambled)
identified as either “low risk,”“moderate risk,”or “problem
gamblers.”The prevalence of PG was 6.3% and moderate-
risk gambling was 8.3%, which is around eight and three
times greater than in the general population, respectively.
One-sample tests confirmed that problem and moderate-risk
gamblers were overrepresented in our sample, relative to the
general population. A further 7.1% were identified as
Table 4. Odds of meeting PGSI criteria for each problem gambling category (relative to non-problem gamblers) for each current psychiatric
diagnosis, after controlling for gender
a
BSEof BWald pOdds ratio 95% CI for odds ratio
Drug-use disorder
Low-risk gambling 0.52 0.50 1.09 .30 1.69 0.63–4.50
Moderate-risk gambling 0.72 0.44 2.69 .10 2.06 0.87–4.87
Problem gambling 1.46 0.40 13.62 <.001 4.31 1.98–9.37
Borderline personality disorder
Low-risk gambling 0.20 0.50 0.17 .68 1.23 0.46–3.25
Moderate-risk gambling 0.15 0.50 0.09 .77 1.16 0.44–3.06
Problem gambling 0.95 0.42 5.05 .02 2.59 1.13–5.94
Bipolar disorder
Low-risk gambling −0.25 0.39 0.41 .52 0.78 0.36–1.68
Moderate-risk gambling 0.06 0.34 0.04 .85 1.07 0.55–2.06
Problem gambling 0.70 0.32 4.66 .03 2.01 1.07–3.80
Psychotic disorder
Low-risk gambling 0.26 0.29 0.85 .36 1.30 0.74–2.28
Moderate-risk gambling 0.73 0.26 8.08 .004 2.08 1.25–3.44
Problem gambling 0.60 0.29 4.23 .04 1.83 1.03–3.25
Alcohol use disorder
Low-risk gambling 0.72 0.51 2.01 .16 2.06 0.76–5.60
Moderate-risk gambling 0.71 0.48 2.22 .14 2.03 0.80–5.16
Problem gambling 0.59 0.56 1.13 .29 1.81 0.60–5.43
Anxiety disorder (any)
Low-risk gambling 0.16 0.27 0.35 .55 1.17 0.69–2.00
Moderate-risk gambling −0.28 0.26 1.21 .27 0.75 0.45–1.25
Problem gambling 0.07 0.29 0.06 .81 1.07 0.61–1.88
Depression
Low-risk gambling −0.05 0.27 0.03 .86 0.95 0.56–1.62
Moderate-risk gambling −0.05 0.25 0.03 .85 0.95 0.58–1.57
Problem gambling −0.21 0.29 0.54 .46 0.81 0.46–1.42
Note. CI: confidence interval; SE: standard error; PGSI: Problem Gambling Severity Index.
a
Seven participants who identified their gender as “other”were excluded from these analyses, as their inclusion led to perfect prediction
errors. Thus, n=830 for these statistics. The model testing whether eating disorders predicted problem gambling categories is excluded from
this table, because there were insufficient participants with both an eating disorder and some levels of gambling problems to allow calculation
of odds ratios and/or confidence intervals for all categories. Bold values reflect the statistically significant findings.
Table 5. Percentage of participants in each PGSI category meeting criteria for harmful substance use, according to substance use
screening tools
Non-gambler/
non-problematic
gambler (n=654)
Low-risk gambler
(n=60)
Moderate-risk
gambler (n=70)
Problem gambler
(n=53
*
)χ
2
(3) p
Nicotine dependence 35.6
a,b
43.3 65.7
c
67.9
c
41.56 <.001
Hazardous drinking 35.6
d
53.3
c
38.6 41.5 7.79 .0505
Drug dependence 8.4
a,b
6.7
b
20.0
c
25.0
c,d
22.70 <.001
Note. PGSI: Problem Gambling Severity Index.
Significant pairwise post-hoc results are indicated by:
a
Differs significantly from moderate-risk gamblers.
b
Differs significantly from problem
gamblers.
c
Differs significantly from non-gamblers/non-problematic gamblers.
d
Differs significantly from low-risk gamblers.
*Data regarding illicit drug dependence were missing for one participant, who was classified as a problem gambler, so n=52 for the bottom
row.
Journal of Behavioral Addictions
Manning et al.
“low-risk”gamblers. These findings provide further
evidence that individuals with mental health disorders have
elevated rates of gambling problems. The rates of problem
and moderate-risk gambling in this sample are slightly
higher than those from other psychiatric outpatient services
that treat patients with a range of psychiatric mental
health disorders (Dowling et al., 2014;Henderson, 2004;
Nehlin et al., 2013;Zimmerman et al., 2006a,2006b). This
may be because of the high prevalence of psychotic
disorders in this sample. Indeed, the rates of problem
gambling and moderate-risk gambling in this study closely
align with the rates identified in patients with psychotic
disorders in outpatient services in Victoria (5.8% and 6.4%,
respectively) (Haydock et al., 2015).
One of the study strengths was the recruitment of patients
with various mental health diagnoses, as well as the assess-
ment of substance use. This permitted analysis of rates
of PG category by current diagnosis as well as levels of
harmful substance use, revealing a number of correlates of
increased vulnerability. In support of the earlier literature,
high rates of harmful substance use were reported among
mental health patients (Croton, 2005;Hartz et al., 2014;
Manning et al., 2008;Zimmermann et al., 2012). The
motivations for substance use among this population
may include the need for enhanced social participation
(e.g., improving social confidence or connecting with
peers), alleviating symptoms, or heightening positive
affect (Kober & Bolling, 2014). Neurocognitive factors
(e.g., altered brain reward systems or inhibitory control and
decision-making) may also increase the likelihood of sub-
stance use (Gregg, Barrowclough, & Haddock, 2007).
This study revealed that patients with drug-use disorder
had over four times the risk of PG, echoing a previous meta-
analysis, which found that 10.0%–43.4% of alcohol and
drug service attendees met criteria for PG (Cowlishaw et al.,
2014). Aside from a self-reported diagnosis of drug-use
disorder, PG also overlapped with other indicators of
vulnerability to addictive behaviors: PGs exhibited signifi-
cantly higher rates of nicotine and illicit drug dependence,
based on standardized screening tools. Indeed high rates of
comorbid substance use and gambling disorders are evident
in the literature, with attention drawn to overlapping clinical,
neurocognitive, and neurobiological features (Grant &
Chamberlain, 2014,2015). However, patients attending
mental health services may be particularly vulnerable to
both substance use and gambling behaviors more broadly,
as a result of socioenvironmental, symptom-related, and
neurobiological factors, such as impulsivity and reward
dysregulation, which increase the likelihood of engaging in
risky reward-seeking behaviors (Carey, Knodt, Conley,
Hariri, & Bogdan, 2017;Dean & Keshavan, 2017;Polter
& Kauer, 2014).
Participants diagnosed with a psychotic disorder, bipolar,
or BPD had double the risk of PG, and psychotic disorder
was the only significant predictor of moderate-risk gam-
bling. While psychiatric symptoms may predict subsequent
problem gambling (Dowling et al., 2017), and problem
gambling may predict subsequent psychiatric disorders
(Chou & Afifi, 2011;Parhami et al., 2014;Pilver et al.,
2013), several factors, such as cognitive impairment, im-
pulsivity, emotion dysregulation, and reward dysregulation,
could underpin these associations. For example, cognitive
impairment, common to bipolar disorder and schizophrenia,
could underpin the observed problem gambling comorbidity
by compromising ability to self-monitor gambling behavior
and losses, consider consequences, and make gambling-
related decisions. Similarly, as with substance use disorders,
impulsivity, and emotional dysregulation are hallmark
and potentially transdiagnostic characteristics of BPD and
bipolar disorder, which may drive risky reward-seeking
behavior, such as excessive gambling. Indeed, impulsivity
(Lorains, Stout, Bradshaw, Dowling, & Enticott, 2014;
Suomi, Dowling, & Jackson, 2014) and emotional dysre-
gulation (de Lisle, Dowling, & Allen, 2012;Jauregui et al.,
2016) are elevated among individuals with gambling pro-
blems. Impulsivity also increases the likelihood of subse-
quent gambling becoming a problem (Dowling et al.,
2017;Liu et al., 2013). Some mental illnesses also involve
altered sensitivity to reward (e.g., hypersensitive in bipolar
vs. hyposensitive in depression) (Alloy, Olino, Freed, &
Nusslock, 2016), which likely influences motivation to
engage in high-reward behaviors such as gambling. Experi-
mental gambling paradigms have found that people with
schizophrenia or BPD make riskier choices and are less
likely to change their behavior in response to negative
feedback (Pedersen, Goder, Tomczyk, & Ohrmann, 2017;
Schuermann, Kathmann, Stiglmayr, Renneberg, & Endrass,
2011). The result of these potentially converging factors is
that when engaging in an activity with high potential
rewards, such as gambling, patients may make impulsive
or poorly reasoned decisions and continue to gamble despite
increasing losses (Grant & Chamberlain, 2014). However,
further research is needed to disentangle the causal links
between these risk factors, psychiatric disorders, and gam-
bling problems.
The findings provide further evidence that individuals
with mental health issues are particularly vulnerable to
moderate risk gambling and PG. Isolation, poor social
support, and stigma are common among individuals with
mental health disorders (Angermeyer, Holzinger, &
Matschinger, 2010;Linz & Sturm, 2013), and loneliness
is a known predictor of PG (Botterill, Gill, McLaren, &
Gomez, 2016). Mental health patients (80% of whom were
single, divorced, or widowed), may gamble to counteract
negative affect arising from these issues. The mean month-
ly spent among PGs ($440) is a potential concern given the
low personal incomes (53% with a fortnightly personal
income of less than $800 before tax) and the high propor-
tion (44%) receiving disability support or other pensions in
this sample. It is likely that among PGs high gambling
expenditure could exacerbate financial difficulties, leading
to personal loans and mounting debt, which could worsen
the psychosocial problems driving patients to gamble in the
first place.
Those experiencing PG should be a priority group in
terms of regular monitoring, since high rates of self-
harm and suicidal behaviors have been established among
individuals with gambling disorders (Moghaddam, Yoon,
Dickerson, Kim, & Westermeyer, 2015). However, given
their high rates of moderate risk and PG, the finding that less
than half (43%) of participants had ever experienced any
clinician enquiry in relation to their gambling behavior at
Journal of Behavioral Addictions
Problem gambling in mental health service patients
their current mental health service was somewhat discon-
certing and highlights the need for clinicians to engage in
routine screening for gambling problems. This is particular-
ly pertinent since over half of all gamblers were at risk of, or
already experiencing gambling problems.
While these findings have important implications, there
are a number of limitations worthy of consideration. While a
large number of patients were surveyed (n=837), this
represented only 55% of the patients attending the services
included in the study. Given the complex and vulnerable
nature of the population being surveyed, high rates of
exclusion or refusal to participate are to be expected, and
clinicians asked researchers to avoid inviting the most
acutely unwell patients to participate due to behavioral risk
issues. Therefore, the estimated prevalence of PG may be
conservative and not representative of those with more
complex and acute conditions. While the use of a validated
interviewer-administered diagnostic tool, such as the
Structured Clinical Interview for DSM-5 Disorders (First,
Williams, Karg, & Spitzer, 2015) to categorize participants’
psychiatric diagnoses would have strengthened the reliabil-
ity of the findings, this would have considerably increased
the time taken to complete the survey and would not have
allowed the questionnaire to be self-completed by partici-
pants. It is also possible that participants underreported their
gambling behavior because of demand characteristics and
social desirability effects. A further limitation is the reliance
on participant self-report where the reporting of substance
use and gambling behaviors could be affected by recall bias.
Finally, despite our efforts to recruit from two regional
sites and a state-wide outreach service, remote/rural clients
were underrepresented in the sample –82% of Victoria’s
population resides in the Melbourne metropolitan area, but
94% of our sample resided in the metropolitan area. Thus,
our findings may not generalize to those residing outside
metropolitan areas.
Despite these limitations, the findings provide important
new insights into gambling problems among patients seek-
ing treatment for mental health disorders. Despite lower
rates of gambling participation, participants were eight times
as likely to be PGs and three times as likely to be moderate-
risk gamblers as adults in the general population, and half of
all gamblers were experiencing some level of PG. The
findings highlight important implications for mental health
services including the need to raise awareness among both
staff and consumers of the increased rates of PG (particu-
larly for those diagnosed with psychotic, drug use, bipolar,
and BPD). Finally, the findings also highlight the impor-
tance of embedding routine screening processes in clinical
practice and ensuring clinical staff are adequately trained to
recognize and respond to PG.
Funding sources: This research was supported by the
Victorian Responsible Gambling Foundation. VM has re-
ceived funding for research from multiple sources over the
past 36 months including the National Health and Medical
Research Council (NHMRC), VicHealth, the Victorian
Department of Health and Human Services (DHHS), and
the Victorian Responsible Gambling Foundation (VRGF).
These include government departments or agencies that are
primarily funded by government departments (some through
hypothecated taxes from gambling revenue). She has not
knowingly received direct funding from the gambling in-
dustry or any industry-sponsored organization.
NAD has received funding from multiple sources, includ-
ing government departments or agencies that are funded
primarily by government departments (some through hypoth-
ecated taxes from gambling revenue). In the previous 36
months, she has received research funding from the VRGF,
the Tasmanian Department of Treasury and Finance, Gam-
bling Research Exchange Ontario (GREO), the New South
Wales Government Department of Premier and Cabinet, the
Hong Kong Research Grants Council, Deakin University, and
the Australian Gambling Research Centre. She was previous-
ly employed at the Problem Gambling Research and Treat-
ment Centre at the University of Melbourne, which was
funded by the VRGF. She has not knowingly received travel
support, speaker honoraria, or research funding from the
gambling industry or any industry-sponsored organization.
SL is a recipient of a NHMRC Early Career Fellowship.
In the past 36 months, he has received funding for research
from the Victorian DHHS, Victorian Women’s Benevolent
Trust, the VRGF and Janssen-Cilag. He has been an invited
speaker for Hospira.
SR does not hold any ongoing position, receive ongoing,
or significant funding, and is not engaged in any business or
organization that creates a conflict of interest (real, per-
ceived, actual, or potential) with the current research. She
has had financial professional dealings with various State
and Federal governments directly and indirectly over the
past 3 years including research funding from organizations
that are funded directly or indirectly from the gambling
industry or levies on the gambling industry including the
VRGF and GREO. She has also received research funding
from the NSW Office of Liquor, Gaming, and Racing,
Australian Institute of Family Studies, and Gambling Re-
search Australia. SR is currently the recipient of a Health
Research Council grant in New Zealand.
JBBG has no conflict of interest to declare. During the
past 36 months, his salary was funded by the NHMRC. RV
has no affiliations with the gambling industry. She receives
research funding from several government agencies, includ-
ing the Massachusetts Gaming Commission, and the Cana-
dian Centre on Substance Abuse. She also receives research
funding from several academic and non-governmental agen-
cies, including the Center for Gambling Studies at Rutgers
University, the Oregon Council on Problem Gambling, and
Turning Point in Victoria, Australia.
JK is employed by the Alfred Hospital, Melbourne. She
has received research grants from government bodies such
as VicHealth and NMHRC as well as Jansen Cilag, Astra
Zeneca and Eli Lily pharmaceuticals, and the VRGF.
DIL has received research grants from the NHMRC and
has provided consultancy advice to Lundbeck and Indivior,
and has received travel support and speaker honoraria from
Astra Zeneca, Janssen, Lundbeck, and Servier.
Authors’contribution: VM contributed to study conception
and design, obtaining funding for this project, study super-
vision, interpretation of data, and drafted the majority of this
manuscript. NAD, SL, SR, RV, JK, and DIL all contributed
to study conception and design, obtaining funding for this
Journal of Behavioral Addictions
Manning et al.
project, project monitoring, and reviewed and edited this
manuscript. JBBG conducted statistical analyses and
assisted with interpretation of these data and drafting sec-
tions of this manuscript.
Conflict of interest: VM, NAD, JBBG, and RV have no
other conflict of interest (whether real or perceived) to
declare in relation to this article.
Acknowledgements: The authors would like to acknowledge
and thank the funder, the Victorian Responsible Gambling
Foundation. The authors would like to thank many research-
ers who assisted with project administration, data collection,
and analysis, including Fiona Barker, Stephanie Merkouris,
Ramez Bathish, Tomas Cartmill, Nyssa Fergusson,
Gabriella Flaks, Mollie Flood, Erin Garde, Andrew Larner,
Mathan Maglaya, Janette Mugavin, Annabeth Simpson,
Laura
Gorrie, Pinar Thorn, Christopher Greenwood, Erin Oldenhof,
and Sam Campbell. The authors are extremely grateful to the
clinicians, team leaders, practice managers, and support
workers who assisted with accessing participants and to the
consumer representatives with lived experience who assisted
with the design of the client survey. Most importantly, we
wish to express our gratitude to the patients of the participat-
ing mental health services for their support and participation
in the study.
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Journal of Behavioral Addictions
Problem gambling in mental health service patients
