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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.
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Problem gambling and substance use in patients attending community mental
health services
Turning Point, Eastern Health, Melbourne, VIC, Australia
Eastern Health Clinical School, Monash University, Melbourne, VIC, Australia
School of Psychology, Deakin University, Geelong, VIC, Australia
Melbourne Graduate School of Education, University of Melbourne, Melbourne, VIC, Australia
Monash Alfred Psychiatry Research Centre, Alfred Health and Monash University Central Clinical School, Melbourne, VIC, Australia
School of Population Health, University of Auckland, Auckland, New Zealand
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 =38.244.9]. The Problem Gambling Severity Index identied 19.7% [CI =17.022.4] as non-problem
gamblers,7.2% [CI =5.48.9] as low-riskgamblers, 8.4% [CI =6.510.2] as moderate-riskgamblers, and
6.3% [CI =4.78.0] as problem gamblers.One-fth (21.9%) of the sample and 52.6% of all gamblers were
identied as either low-risk, moderate-risk, or problem gamblers (PGs). Patients classied as problem and moderate-
risk gamblers had signicantly elevated rates of nicotine and illicit drug dependence (p<.001) according to short
screening tools. Current diagnosis of drug use (OR =4.31 [CI =1.989.37]), borderline personality (OR =2.59
[CI =1.135.94]), bipolar affective (OR =2.01 [CI =1.073.80]), and psychotic (OR =1.83 [CI =1.033.25])
disorders were signicant predictors of problem gambling. Discussion and conclusions: Patients were less likely
to gamble, but eight times as likely to be classied as PG, relative to Victorias adult general population. Elevated
rates of harmful substance use among moderate-risk and PG suggest overlapping vulnerability to addictive behaviors.
These ndings 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
While gambling is a popular pastime for many individuals, it
remains a signicant 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) reclassied path-
ological gamblingas Gambling Disorderunder 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% identied 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@
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 signicant
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 specicdis-
orders. Haydock, Cowlishaw, Harvey, and Castle (2015) iden-
tied 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-
tied 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 nance 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,
nancial and social difculties; 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 specicpsy-
chiatric diagnoses among patients attending a diverse range
of mental health services in Victoria, Australia, including, for
the rst time, patients attending private mental health services.
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.
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 nal 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 yeswere 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-
ties non-PG(a score of 0), low-risk gambling(a score of
12, indicating low-level problems with few or no identied
negative consequences), moderate-risk gambling(score of
37, indicating moderate-level problems leading to some
negative consequences), and PG(a score of 827, 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 denition) 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
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 specic 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 Identi-
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.
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 12 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 condential 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 participants verbal responses into the tablet, in a
quiet secluded area/room away from the main waiting room
to maintain the patients condentiality, 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
Gender (n=837)
Male 50.9 (426)
Female 48.3 (404)
Other 0.8 (7)
Age (n=825) 38 (13), 1895
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
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
version 22.
All prevalence estimates for gambling participation,
low risk, moderate risk, and PG are reported with exact
binomial 95% condence intervals. Using one-sample χ
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 signicance of differences in propor-
tions (e.g., across demographic variables) was examined
using Pearsonsχ
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 specic mental health diagnoses
were predictors of PGSI category after controlling for gender,
with adjusted odds ratios and 95% condence 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 signicantly 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 Pearsonsχ
with pairwise Bonferroni post-hoc comparisons examined in
the case of statistically signicant overall χ
test results.
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
Ofce 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.
Demographic characteristics
Sample characteristics are shown in Table 1. Approximately
half (50.9%) of participants were male, 48.3% were female,
and 0.8% identied their gender as other.The mean age
was 38 (SD =13) years, 77.8% were Australian-born,
10.5% identied as belonging to an ethnic minority, and
2.0% identied 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 rafes, which were not assessed) was 41.6%
[95% CI =38.244.9] and was signicantly higher among
male (n=198, 46.5%) than female participants [n=148,
36.6%; χ
(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
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
22.5 188 17.6 147
Social anxiety 21.0 176 16.6 139
Post-traumatic stress disorder 15.7 131 12.5 105
Obsessivecompulsive 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 =027). Of the total sample, 165 (19.7% [95%
CI =17.022.4]) had a PGSI score in the non-PG range,
60 (7.2% [CI =5.48.9]) had a score in the low-risk range,
70 (8.4% [CI =6.510.2]) had a score in the moderate-risk
range, and 53 (6.3% [CI =4.78.0]) were identied as PGs.
The proportion of participants categorized as low risk,
moderate risk, or PG signicantly differed from estimated
rates in the Victorian general population [χ
(3) =419.51,
p<.001]. Dichotomous contrasts comparing each category
with all others indicated that PG [χ
(1) =317.66, p<.001]
and moderate-risk gambling [χ
(1) =95.87, p<.001] were
more prevalent among participants, whereas low-risk gam-
bling was (non-signicantly) less prevalent [χ
(1) =3.13,
p=.077] among participants than in the Victorian adult
general population. One in ve participants (21.9%; 52.6%
of all gamblers) was identied 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
signicant 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
signicantly 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 signicantly
differ from each other.
Mental health diagnosis by PG severity
Multinomial logistic regression analysis was conducted
to examine associations between specic current psychiatric
diagnoses and PG category. These analyses were conducted
controlling for gender, since gambling prevalence signi-
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 signi-
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 identied 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) identied 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
Substance use by PG severity
Rates of nicotine dependence (according to HSI score) and
illicit drug dependence (according to SDS score) signi-
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-signicant trend for
differences across the PG categories, although in this in-
stance, it was low-risk gamblers that had the highest pro-
portion identied as hazardous drinkers.
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 rafes, 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 ndings 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 ve participants (half of those who gambled)
identied 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 conrmed that problem and moderate-risk
gamblers were overrepresented in our sample, relative to the
general population. A further 7.1% were identied 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
BSEof BWald pOdds ratio 95% CI for odds ratio
Drug-use disorder
Low-risk gambling 0.52 0.50 1.09 .30 1.69 0.634.50
Moderate-risk gambling 0.72 0.44 2.69 .10 2.06 0.874.87
Problem gambling 1.46 0.40 13.62 <.001 4.31 1.989.37
Borderline personality disorder
Low-risk gambling 0.20 0.50 0.17 .68 1.23 0.463.25
Moderate-risk gambling 0.15 0.50 0.09 .77 1.16 0.443.06
Problem gambling 0.95 0.42 5.05 .02 2.59 1.135.94
Bipolar disorder
Low-risk gambling 0.25 0.39 0.41 .52 0.78 0.361.68
Moderate-risk gambling 0.06 0.34 0.04 .85 1.07 0.552.06
Problem gambling 0.70 0.32 4.66 .03 2.01 1.073.80
Psychotic disorder
Low-risk gambling 0.26 0.29 0.85 .36 1.30 0.742.28
Moderate-risk gambling 0.73 0.26 8.08 .004 2.08 1.253.44
Problem gambling 0.60 0.29 4.23 .04 1.83 1.033.25
Alcohol use disorder
Low-risk gambling 0.72 0.51 2.01 .16 2.06 0.765.60
Moderate-risk gambling 0.71 0.48 2.22 .14 2.03 0.805.16
Problem gambling 0.59 0.56 1.13 .29 1.81 0.605.43
Anxiety disorder (any)
Low-risk gambling 0.16 0.27 0.35 .55 1.17 0.692.00
Moderate-risk gambling 0.28 0.26 1.21 .27 0.75 0.451.25
Problem gambling 0.07 0.29 0.06 .81 1.07 0.611.88
Low-risk gambling 0.05 0.27 0.03 .86 0.95 0.561.62
Moderate-risk gambling 0.05 0.25 0.03 .85 0.95 0.581.57
Problem gambling 0.21 0.29 0.54 .46 0.81 0.461.42
Note. CI: condence interval; SE: standard error; PGSI: Problem Gambling Severity Index.
Seven participants who identied their gender as otherwere 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 insufcient participants with both an eating disorder and some levels of gambling problems to allow calculation
of odds ratios and/or condence intervals for all categories. Bold values reect the statistically signicant ndings.
Table 5. Percentage of participants in each PGSI category meeting criteria for harmful substance use, according to substance use
screening tools
gambler (n=654)
Low-risk gambler
gambler (n=70)
Problem gambler
(3) p
Nicotine dependence 35.6
43.3 65.7
41.56 <.001
Hazardous drinking 35.6
38.6 41.5 7.79 .0505
Drug dependence 8.4
22.70 <.001
Note. PGSI: Problem Gambling Severity Index.
Signicant pairwise post-hoc results are indicated by:
Differs signicantly from moderate-risk gamblers.
Differs signicantly from problem
Differs signicantly from non-gamblers/non-problematic gamblers.
Differs signicantly from low-risk gamblers.
*Data regarding illicit drug dependence were missing for one participant, who was classied as a problem gambler, so n=52 for the bottom
Journal of Behavioral Addictions
Manning et al.
low-riskgamblers. These ndings 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 identied 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 condence 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 signi-
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 signicant 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 inuences 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 ndings 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 nancial difculties, leading
to personal loans and mounting debt, which could worsen
the psychosocial problems driving patients to gamble in the
rst 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 nding 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 ndings 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 ndings, 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 Victorias
population resides in the Melbourne metropolitan area, but
94% of our sample resided in the metropolitan area. Thus,
our ndings may not generalize to those residing outside
metropolitan areas.
Despite these limitations, the ndings 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
ndings 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 ndings 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 Womens 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 signicant funding, and is not engaged in any business or
organization that creates a conict of interest (real, per-
ceived, actual, or potential) with the current research. She
has had nancial 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 Ofce 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 conict of interest to declare. During the
past 36 months, his salary was funded by the NHMRC. RV
has no afliations 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.
Authorscontribution: 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.
Conict of interest: VM, NAD, JBBG, and RV have no
other conict 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,
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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Problem gambling in mental health service patients
... Revisiones sistemáticas, predominantemente de Estados Unidos, informan una comorbilidad de JP y trastorno por uso de sustancias (TUS) del 57,5% en la población general y hasta del 22,2% en pacientes en tratamiento en unidades clínicas (Dowling et al., 2018). Varias revisiones sistemáticas y metaanálisis indican que los problemas de juego en pacientes con TUS están sobrerrepresentados (10,0%-43,4%) (Cowlishaw, Merkouris, Chapman y Radermacher, 2014;Himelhoch et al., 2015;Lorains, Stout, Bradshaw, Dowling y Enticott, 2014;Manning et al., 2017). A pesar de esto, incluso estudios más estrictos evidencian tasas de prevalencia del 14% en JP y del 23% si nos referimos a todo el espectro de juego (Cowlishaw et al., 2014). ...
... A pesar de ello, existen datos que sugieren que las tasas de cribado realizadas por los médicos en estos servicios siguen siendo muy bajas (Cowlishaw et al., 2014;Holtgraves, 2009). Se han identificado diferentes barreras ante la realización del cribado y la detección de JP en pacientes con TUS en estos centros, entre ellas la falta de tiempo, la falta de conocimientos para realizarlo, la poca información sobre su eficacia, la percepción de que los problemas relacionados con el juego no son una enfermedad, la falta de intervenciones efectivas o el acceso limitado a unidades de tratamiento específicas (Dowling et al., 2019;Manning et al., 2017). ...
... Al analizar el poder de detección en la población con riesgo de JP, se observa un aumento en todos los parámetros, lo que coincide con los resultados señalados por otros autores (Dowling et al., 2018;Manning et al., 2017), donde ya se identificó un mayor poder para identificar y clasificar correctamente. ...
Problematic Gambling or Gambling Disorder (GD) can act by initiating and maintaining the problem of substance addiction. Despite this, there are no rapid screening tools validated in Spanish. The Brief Problem Gambling Screen (BPGS) has proven to be one of the most sensitive tools for detecting GD and populations at risk. This study aims to validate the Spanish version of the original five-item BPGS. A sample of 100 Spanish-speaking adults with substance use disorder were recruited from an addiction treatment center. The participants were administered the Spanish version of BPGS. It showed strong item reliability properties (Ω = 0.93). Sensitivity and specificity values were excellent (0.93 each), also positive (0.7) and negative (0.99) predictive values suggest high discriminant power when compared to non-GD subjects. Statistically significant strong correlation with a gold-standard measure (Problem Gambling Severity Index) was found (r = 0.8, p < 0.01). Similar psychometric properties were found in at-risk gambler patients. In conclusion, the BPGS seems to be an adequate screening instrument in Spanish-speaking clinical population, and also identifies at-risk of GD subjects.
... [7,8]. Furthermore, people with gambling problems are over-represented in both mental health [9][10][11][12] and alcohol and other drug problem treatment settings [13,14]. Associations with mental health and substance use problems have also been reported early in development, in adolescence and young adulthood [15][16][17][18][19][20]. ...
... Scores range 0-27, with higher scores indicative of greater problem gambling severity. These scores can be categorised into non-problem gambling (scores of 0), low-risk gambling (scores of 1-2), moderate-risk gambling (scores of 3-7) and problem gambling (scores of [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. In previous research, the PGSI has demonstrated high internal consistency, validity, sensitivity, and specificity [2]. ...
... These findings also highlight the need for regular screening for gambling problems within alcohol and other drug treatment services, in order to identify at-risk individuals and provide appropriate resources and referrals [32,66]. It might also suggest the need for up-skilling alcohol and other drug treatment service providers in the delivery of brief and targeted interventions for individuals with co-occurring substance use and gambling problems [11]. ...
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Little is known about the cumulative effect of adolescent and young adult mental health difficulties and substance use problems on gambling behaviour in adulthood. We use data from one of Australia’s longest running studies of social and emotional development to examine the extent to which: (1) mental health symptoms (depressive and anxiety symptoms) and substance use (weekly binge drinking, tobacco, and cannabis use) from adolescence (13–18 years) into young adulthood (19–28 years) predict gambling problems in adulthood (31–32 years); and (2) risk relationships differ by sex. Analyses were based on responses from 1365 adolescent and young adult participants, spanning seven waves of data collection (1998–2014). Persistent adolescent to young adult binge drinking, tobacco use and cannabis use predicted gambling at age 31–32 years (OR = 2.30–3.42). Binge drinking and tobacco use in young adulthood also predicted gambling at age 31–32 years (OR = 2.04–2.54). Prior mental health symptoms were not associated with gambling and no risk relationships differed by sex. Findings suggest that gambling problems in adulthood may be related to the earlier development of other addictive behaviours, and that interventions targeting substance use from adolescence to young adulthood may confer additional gains in preventing later gambling behaviours.
... The mean age of the studied cases was found to be 38±13 years. 12 In the present study, more than half of the participants, that is, 43 (64.2) was employed that this finding was consistent with study by Saddichha et al. 13 Majority of the participants were unmarried as most participants were from age group of 20-30 years, about 44.8% were married. The relationship between marital status and mood disorders is very complex. ...
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Background: Mood disorder is one of the common causes, for which psychiatric consultations are sought. A better understanding of the factors that lead to repeated admissions, increased relapse rates, and concurrent substance dependence is necessary for the development of interventions that may reduce the likelihood of adverse effects in patient with bipolar I disorder. Aims and Objectives: The aims of this study were as follows: (1) To study the correlation between the reasons for relapse and substance use with various sociodemographic factors in patients having bipolar I disorders and (2) to find out the correlation between reasons for relapse, substance use, and severity of illness. Materials and Methods: This was a cross-sectional single interview study, in which 67 consecutive patients having bipolar I disorder and admitted in psychiatry ward were admitted on the basis of a pre-defined inclusion and exclusion criteria. The details about psychiatric, relapse-related symptoms, substance use-related symptoms, as well as other aspects of clinical profile were taken. Results: Out of 67 patients, 51 (76.1%) were male and 16 (23.8%) were female. Mean age of the participants was 30.43 years (S.D.=9.05). Sleep disturbance was seen in all patients (67) and was also the most common symptom. It was followed by psychomotor disturbances in 60 (89.5%) of the patients. Among 67 patients included in the study, substance use was seen among 28 (41.7%) of the patients. Substance use was significantly associated with age of the patients and occupation (P=0.03 each). The age group of more than 30 years (57.7%) had significantly higher substance use compared to those
... From a genetic perspective, family, twin, and adoption studies estimated the heritability (i.e., the overall genetic contribution) of addictions to be 30-70% [35][36][37][38]. Studies also suggested genetic overlaps between behavioral (e.g., gambling) and substance (e.g., alcohol, tobacco, cannabis, and stimulant) addictions [39][40][41][42]. Furthermore, the genetic and environmental overlap between different types of substance and behavioral addictions has long been suggested [43,44]. ...
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Epidemiological and phenomenological studies suggest shared underpinnings between multiple addictive behaviors. The present genetic association study was conducted as part of the Psychological and Genetic Factors of Addictions study (n = 3003) and aimed to investigate genetic overlaps between different substance use, addictive, and other compulsive behaviors. Association analyses targeted 32 single-nucleotide polymorphisms, potentially addictive substances (alcohol, tobacco, cannabis, and other drugs), and potentially addictive or compulsive behaviors (internet use, gaming, social networking site use, gambling, exercise, hair-pulling, and eating). Analyses revealed 29 nominally significant associations, from which, nine survived an FDRbl correction. Four associations were observed between FOXN3 rs759364 and potentially addictive behaviors: rs759364 showed an association with the frequency of alcohol consumption and mean scores of scales assessing internet addiction, gaming disorder, and exercise addiction. Significant associations were found between GDNF rs1549250, rs2973033, CNR1 rs806380, DRD2/ANKK1 rs1800497 variants, and the “lifetime other drugs” variable. These suggested that genetic factors may contribute similarly to specific substance use and addictive behaviors. Specifically, FOXN3 rs759364 and GDNF rs1549250 and rs2973033 may constitute genetic risk factors for multiple addictive behaviors. Due to limitations (e.g., convenience sampling, lack of structured scales for substance use), further studies are needed. Functional correlates and mechanisms underlying these relationships should also be investigated.
... Findings from the study also highlight the need to incorporate gambling screening and a gambling-specific brief intervention protocol (i.e., SBIRT) into a variety of systems serviced by social workers. Significantly higher rates of gambling disorder have been reported in homeless populations (Matheson et al., 2021(Matheson et al., , 2014, some ethnic minority groups , military service members (Van der Maas & Nower, 2021), those seeking psychiatric and/or addiction services (Manning et al., 2017;Sundqvist & Rosendahl, 2019) and across the context of poverty (See, Hahmann et al., 2020 for a scoping review.) Social workers practice across these social service systems and are ideally situated to identify potential gambling problems in time to mitigate adverse consequences that impact not only the individual who gambles but also their families, employers, and communities. ...
Despite the rapid expansion of legalized gambling, few social workers are trained to identify problem gambling symptoms. This study explored gambling knowledge, behavior, and problem symptoms in a sample of 1,777 clinical social workers through an online survey. Findings indicate about 77% of social workers gambled and more than 4% of those who gambled reported at least one problem gambling symptom. Participants answered less than half of the knowledge questions correctly, and a majority were unaware of the current diagnostic classification for gambling disorder or the legal age for gambling. Results of a multivariate regression analysis found that social workers in practice 8 to 15 years, employed in substance treatment facilities or universities, and/or with training in gambling treatment had higher levels of knowledge about gambling and gambling treatment. Findings underscore the need for social work schools and organizations to prioritize education and training for problem gambling identification and treatment.
... [11] However, efforts should be made to create public awareness of the deleterious effects of uncontrolled online gambling and substance use on an individual's physical and mental wellbeing in Nigeria. [12] Moreover, frequent psychiatric services should be offered to individuals who have a medical history of GD, or who are at-risk of GD, or who are currently suffering from GD. [13] The Nigerian government should endeavor to improve the mental health-care quality and standards in the country and ensure stringent regulation of online gambling activities. [14] However, apart from mental health interventions (e.g., cognitive behavioral therapy), lifestyle interventions (e.g., physical activity, smoking/alcohol cessation, and dietary modification) could help to improve the wellbeing and quality of life among patients with GD. [15,16] Virtual gaming is very popular in Nigeria because it is available 24 h a day, 7 days a week, and it is considered riskier by gamblers compared to staking on live fixtures. ...
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There is a lack of detailed information on the epidemiology, prevention, and treatment of gambling disorder (GD) in Nigeria, a country that has the second-largest online sports gaming market in Africa. The objective of this review article was to emphasize the need for a nationwide epidemiological study on the GD caused by compulsive sports betting in Nigeria. The literature searches were carried out using the PubMed, Scopus, and Web of Science electronic databases regarding articles concerning sports betting, problem gambling, GD, and mental health in Nigeria using the following medical subject headings: Pathological Gambling AND Gamblings AND Federal Republic of Nigeria AND Social Epidemiology. A nationwide surveillance is necessary to determine the epidemiology, risk factors, and prevention and treatment measures for GD in Nigeria. To prevent negative public health consequences, sports gambling activities in Nigeria should be restricted and controlled. There is a need to set aside strict measures to control both legal and illegal online and offline sports betting activities in Nigeria. Moreover, the implementation of an effective mental health-care delivery policy to tackle gambling addiction and associated psychiatric comorbidities in Nigeria will enable efficient diagnosis and treatment of individuals with GD and improve relapse prevention. A well-funded and functional mental health-care system and research framework will facilitate effective mental health-care delivery among individuals at risk of GD and patients with GD in Nigeria. Expanding access to and use of mental health treatment services could substantially reduce the prevalence of GD and associated comorbidities in Nigeria.
... We also found lower tobacco use and fewer psychiatric comorbidities in OSB compared to SM gamblers. The smoking prevalence in our OSB sample is slightly lower than in previous studies among online gamblers (18)(19)(20). This could be because almost half of OSB gamblers from our study have reached a university education level that has shown a negative association with smoking prevalence (21). ...
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Background: Gambling landscape has changed in recent years with the emergence of online gambling (OG). Greater accessibility and availability of this betting modality can increase the risk of developing a gambling disorder (GD). Online sports betting (OSB) is currently the most common type of OG, but little is known about the clinical characteristics of OSB compared to slot-machine (SM) gamblers, the most common offline gambling disorder. Methods: This was a prospective study conducted between October 2005 and September 2019, and included outpatients diagnosed with GD seen in a Pathological Gambling and Behavioral Addictions referral unit. Only patients with OSB and SM disorders were included. The main objective was to assess the clinical profile of OSB compared to SM gamblers, and to define clinical predictors for developing OSB gambling disorder. Logistic regression was performed to determine the effects of variables on the likelihood of this disorder. Results: Among 1,186 patients attended in our Unit during the study period, 873 patients were included; 32 (3.7%) were OSB gamblers and 841 (96.3%) were SM gamblers. Overall, mean age was 45 ± 13 years and 94.3% were men. Compared to SM patients, OSB patients were younger (34.9 ± 9.5 vs. 45.3 ±13), more frequently single (43.8 vs. 20.6%) and had a university education level (43.8 vs. 4.5%); they were also more frequently non-smokers (18.7 vs. 66.7%) and had fewer psychiatric comorbidities (12.5 vs. 29.4%) than SM gamblers. GD duration before treatment initiation was shorter in OSB patients than in SM gamblers, most of them (81.3 vs. 42.4%) with ≤ 5 years of GD duration. OSB gamblers showed significant differences in weekly gambling expenditure, spending higher amounts than SM patients. Younger age (OR: 0.919; 95% CI: 0.874–0.966), university education level (OR: 10.658; 95% CI: 3.330–34.119), weekly expenditure >100€ (OR: 5.811; 95% CI:1.544–21.869), and being a non-smoker (OR:13.248; 95% CI:4.332–40.517) were associated with an increased likelihood of OSB gambling behavior. Conclusions: We identified different profiles for OSB and SM gamblers. Younger age, university education level, higher weekly expenditure, and non-smoking habit were associated with OSB compared to SM disorders. Prevention strategies should help young people become aware of the severe risks of OSB.
... Le comorbilità maggiormente riscontrate sono i disturbi dell'umore, i disturbi d'ansia, la dipendenza da sostanze, l'uso e la dipendenza da alcol e da nicotina (Petry, 2005;Hartmann e Blaszczynski, 2016). Alcuni studi hanno evidenziato che avere una diagnosi di abuso di sostanze, di disturbo di personalità borderline, di disturbo bipolare o di psicosi è un fattore predittivo della presenza di gioco d'azzardo problematico (Manning et al., 2017). Altre associazioni significative sono state rilevate tra i problemi di gioco d'azzardo e i disturbi del controllo degli impulsi, come ad esempio l'acquisto compulsivo, la cleptomania o il comportamento sessuale compulsivo (Derbyshire e Grant, 2015). ...
Gambling disorder (GD) is an important public health problem, with high prevalence rates. A review of studies investigating the relationship between problem gambling and suicidality (suicidal ideation, suicidal attempts and death by suicide) has been conducted. The PsycInfo and PubMed databases were used by entering gambl* AND suicid* as keywords. 35 studies were included in the review. Individual factors associated with suicidality in gamblers are as follows: female gender, older age and considerable number of years of gambling, low level of education, presence of debts, parental gambling, impulsivity, emotional dysregulation, sensation-seeking, harm avoidance, immature defense style, adverse life events. Specific type of gambling activities related to suicide risk were also found including electronic gaming machines, sport bets, daily fantasy sports, and instant lotteries. Even though suicide risk was found among sub-syndromic GD subjects, higher suicidality is related to severity of gambling problems. Finally, disordered gamblers who have psychiatric comorbidities (e.g., mood disorders, anxiety disorders, Cluster B personality disorders, eating disorders, substance use disorders) appear to be at higher risk than those with uncomplicated GD. In conclusion, these results can be used to improve gambling assessment and to plan targeted treatments. They are also essential for the prevention of suicidal behavior.
... However, while they displayed high levels of knowledge, their confidence in responding to PG was generally low, as was the frequency with which they screen for PG. This is of concern given previous studies have concluded that mental health clinicians have a significant role in the identification and management of PG [20]. Indeed, the majority of the current sample reported that 10%-25% of their caseload was affected by gambling. ...
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Gambling problems commonly co-occur with other mental health problems. However, screening for problem gambling (PG) rarely takes place within mental health treatment settings. The aim of the current study was to examine the way in which mental health clinicians respond to PG issues. Participants (n = 281) were recruited from a range of mental health services in Victoria, Australia. The majority of clinicians reported that at least some of their caseload was affected by gambling problems. Clinicians displayed moderate levels of knowledge about the reciprocal impact of gambling problems and mental health but had limited knowledge of screening tools to detect PG. Whilst 77% reported that they screened for PG, only 16% did so “often” or “always” and few expressed confidence in their ability to treat PG. However, only 12.5% reported receiving previous training in PG, and those that had, reported higher levels of knowledge about gambling in the context of mental illness, more positive attitudes about responding to gambling issues, and more confidence in detecting/screening for PG. In conclusion, the findings highlight the need to upskill mental health clinicians so they can better identify and manage PG and point towards opportunities for enhanced integrated working with gambling services.
Introduction Mutual support groups (e.g. SMART Recovery) are an important source of support for people experiencing addictive behaviours. Little is known about the use of mutual support groups by people who use methamphetamine, or the factors that may influence group cohesion. Methods This study uses post-group data reported by SMART Recovery facilitators in Australia between 2018 and 2020. Group cohesion was indexed by facilitator ratings of The Group Entitativity measure (GEM-GP). Participant characteristics (gender, age, new or returning group member, voluntary or mandated attendance) and group location (major city vs. regional/remote vs. online) were used to (a) compare methamphetamine and non-methamphetamine related attendances; and (b) explore relationships to group cohesion within groups where the majority attended for methamphetamine. Results Methamphetamine use was the second most common reason for attending SMART Recovery groups (n = 4929; 22.2% service occasions). Methamphetamine-related service occasions were more likely amongst men, people aged <45 years, returning attendees and regional/rural groups (allp < .05). GEM-GP scores were high (signalling strong cohesion), and did not significantly differ according to proportion of participants attending for methamphetamine (F(1,2) = 0.482, p = .618). Group cohesion increased with larger group size, proportion of women and proportion of younger people (F(4, 504) = 11.058, p < .001)). Discussion and Conclusions This study improves current understanding of service utilisation by people who use methamphetamine. SMART Recovery groups offer an avenue for supporting a diverse range of people who use methamphetamine, outside the formal treatment system. This provides an important foundation for improving community support options for people who use methamphetamine.
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This systematic review aimed to identify early risk and protective factors (in childhood, adolescence or young adulthood) longitudinally associated with the subsequent development of gambling problems. A systematic search of peer-reviewed and grey literature from 1990 to 2015 identified 15 studies published in 23 articles. Meta-analyses quantified the effect size of 13 individual risk factors (alcohol use frequency, antisocial behaviours, depression, male gender, cannabis use, illicit drug use, impulsivity, number of gambling activities, problem gambling severity, sensation seeking, tobacco use, violence, undercontrolled temperament), one relationship risk factor (peer antisocial behaviours), one community risk factor (poor academic performance), one individual protective factor (socio-economic status) and two relationship protective factors (parent supervision, social problems). Effect sizes were on average small to medium and sensitivity analyses revealed that the results were generally robust to the quality of methodological approaches of the included articles. These findings highlight the need for global prevention efforts that reduce risk factors and screen young people with high-risk profiles. There is insufficient investigation of protective factors to adequately guide prevention initiatives. Future longitudinal research is required to identify additional risk and protective factors associated with problem gambling, particularly within the relationship, community, and societal levels of the socio-ecological model.
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This article examines gambling harms from both gamblers and affected others’ perspectives. Participants (3076 gamblers and 2129 affected others) completed a retro- spective survey that elicited information on harms they experienced from gambling across their lifetime. Their responses were analyzed through testing measurement invariance, estimating item-response theoretic parameters, calculating percentages, confidence inter- vals, and correlations, as well as regressions. The results indicated large commonalities in the experience of harms reported by gamblers and affected others. Further, gamblers appeared to ‘export’ about half of the harms they experienced to those around them. The findings also provided detailed profiles of evolving harms as problem gambling severity varies
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Many studies carried out on treatment-seeking problem gamblers (PG) have reported high levels of comorbid substance use disorders, and mental and physical health problems. Nevertheless, general population studies are still sparse, most of them have been carried out in the United States or Canada, and gender differences have not always been considered. Thus, the aim of this study was to describe the type of games, and psychological and physical correlates in male and female PG in a nationally representative French sample. The total sample studied involved 25,647 subjects aged 15–85 years, including 333 PG and 25,314 non-problem gamblers (NPG). Data were extracted from a large survey of a representative sample of the French general population. They were evaluated for sociodemographic variables, gambling behavior, type of gambling activity, substance use, psychological distress, body mass index, chronic disease, and lack of sleep. Overall, there were significant differences between PG and NPG in gender, age, education, employment and marital status, substance use disorders (alcohol, tobacco, cannabis, cocaine and heroin), psychological distress, obesity, lack of sleep and type of gambling activity. Although male and female PG had different profiles, the gambling type, especially strategic games, appeared as an important variable in the relationship between gender and problem gambling. This research underlines the importance of considering gender differences and gambling type in the study of gambling disorders. Identifying specific factors in the relationship between gender, gambling type and gambling problems may help improve clinical interventions and health promotion strategies.
Background Impulsivity is generally considered a core feature of psychopathy, however one problem with understanding the association between these constructs is that both are multifaceted. Existing research often treats one or both of these constructs as unidimensional with important information regarding the complex nature of the relationship being lost. To clarify this issue the present study employs a canonical correlation analysis (CCA) which allows for the comparison of two multifaceted measurement scales simultaneously. Methods Respondents (n = 970) completed the Barratt Impulsiveness Scale (BIS-11) and the Psychopathic Personality Inventory (PPI). CCA was performed to explore the strength and nature of the association between impulse control and psychopathy. Results There was a large correlation (r = 0.57) between BIS-11 and PPI total scores. Further exploration using CCA showed that 70.2% of the variance was shared between the subscales, and three significant canonical functions emerged. These were found to be interpretable and suggest that impulsivity relates to the broader psychopathy domain in a complex fashion, and that non-planning impulsivity may be the primary trait which distinguishes between psychopathy subtypes. Discussion The findings support a complex multi-dimensional relationship between impulsivity and psychopathy. The simple impulsivity-psychopathy correlation has much less explanatory power than has a multivariate approach.
Major Depressive Disorder (MDD) is one of the most common and debilitating mental disorders; however its etiology remains unclear. This paper aims to summarize the major neurobiological underpinnings of depression, synthesizing the findings into a comprehensive integrated view. A literature review was conducted using Pubmed. Search terms included “depression” or “MDD” AND “biology," “neurobiology," “inflammation," “neurogenesis," “monoamine," and “stress." Articles from 1995-2016 were reviewed with a focus on the connection between different biological and psychological models. Some possible pathophysiological mechanisms of depression include altered neurotransmission, HPA axis abnormalities involved in chronic stress, inflammation, reduced neuroplasticity, and network dysfunction. All of these proposed mechanisms are integrally related and interact bidirectionally. In addition, psychological factors have been shown to have a direct effect on neurodevelopment, causing a biological predisposition to depression, while biological factors can lead to psychological pathology as well. The authors suggest that while it is possible that there are several different endophenotypes of depression with distinct pathophysiological mechanisms, it may be helpful to think of depression as one united syndrome, in which these mechanisms interact as nodes in a matrix. Depressive disorders are considered in the context of the RDoC paradigm, identifying the pathological mechanisms at every translational level, with a focus on how these mechanisms interact. Finally, future directions of research are identified.
Background: Problematic alcohol use in adolescence and adulthood is a common and often debilitating correlate of childhood attention-deficit/hyperactivity disorder (ADHD). Converging evidence suggests that ADHD and problematic alcohol use share a common additive genetic basis, which may be mechanistically related to reward-related brain function. In the current study, we examined whether polygenic risk for childhood ADHD is linked to problematic alcohol use in young adulthood through alterations in reward-related activity of the ventral striatum, a neural hub supporting appetitive behaviors and reinforcement learning. Methods: Genomic, neuroimaging, and self-report data were available for 404 non-Hispanic European-American participants who completed the ongoing Duke Neurogenetics Study. Polygenic risk scores for childhood ADHD were calculated based on a genome-wide association study meta-analysis conducted by the Psychiatric Genomics Consortium and tested for association with reward-related ventral striatum activity, measured using a number-guessing functional magnetic resonance imaging paradigm, and self-reported problematic alcohol use. A mediational model tested whether ventral striatum activity indirectly links polygenic risk for ADHD to problematic alcohol use. Results: Despite having no main effect on problematic alcohol use, polygenic risk for childhood ADHD was indirectly associated with problematic alcohol use through increased reward-related ventral striatum activity. Conclusions: Individual differences in reward-related brain function may, at least in part, mechanistically link polygenic risk for childhood ADHD to problematic alcohol use.
Background and objectives: Patients with schizophrenia reveal impaired decision-making strategies causing social, financial and health care problems. The extent to which deficits in decision-making reflect intentional risky choices in schizophrenia is still under debate. Based on previous studies we expected patients with schizophrenia to reveal a riskier performance on the GDT and to make more disadvantageous decisions on the IGT. Methods: In the present study, we investigated 38 patients with schizophrenia and 38 matched healthy control subjects with two competing paradigms regarding feedback: (1) The Game of Dice Task (GDT), in which the probabilities of winning or losing are stable and explicitly disclosed to the subject, to assess decision-making under risk and (2) the Iowa Gambling Task (IGT), which requires subjects to infer the probabilities of winning or losing from feedback, to investigate decision-making under ambiguity. Results: Patients with schizophrenia revealed an overall riskier performance on the GDT; although they adjusted their strategy over the course of the GDT, they still made significantly more disadvantageous choices than controls. More positive symptoms in patients with schizophrenia indicated by higher PANSS positive scores were associated with riskier choices and less use of negative feedback. Compared to healthy controls, they were not impaired in net score but chose more disadvantageous cards than controls on the first block of the IGT. Limitations: Effects of medication at the time of testing cannot be ruled out. Conclusions: Our findings suggest that patients with schizophrenia make riskier decisions and are less able to regulate their decision-making to implement advantageous strategies, even when the probabilities of winning or losing are explicitly disclosed. The dissociation between performance on the GDT and IGT suggests a pronounced impairment of executive functions related to the dorsolateral prefrontal cortex.
Background: Gambling problems co-occur frequently with other psychiatric difficulties and may complicate treatment for affective disorders. This study evaluated the prevalence and correlates of gambling problems in a U. S. representative sample reporting treatment for mood problems or anxiety. Methods: n=3007 respondents indicating past-year treatment for affective disorders were derived from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Weighted prevalence estimates were produced and regression analyses examined correlates of gambling problems. Results: Rates of lifetime and past-year problem gambling (3+DSM-IV symptoms) were 3.1% (95% CI=2.4-4.0%) and 1.4% (95% CI=0.9-2.1%), respectively, in treatment for any disorder. Rates of lifetime problem gambling ranged from 3.1% (95% CI=2.3-4.3%) for depression to 5.4% (95% CI=3.2-9.0%) for social phobia. Past-year conditions ranged from 0.9% (95% CI=0.4-2.1%) in dysthymia to 2.4% (95% CI=1.1-5.3%) in social phobia. Higher levels were observed when considering a spectrum of severity (including 'at-risk' gambling), with 8.9% (95% CI=7.7-10.2%) of respondents indicating a history of any gambling problems (1+ DSM-IV symptoms). Lifetime gambling problems predicted interpersonal problems and financial difficulties, and marijuana use, but not alcohol use, mental or physical health, and healthcare utilisation. Limitations: Data were collected in 2001-02 and were cross-sectional. Conclusions: Gambling problems occur at non-trivial rates in treatment for affective disorders and have mainly psychosocial implications. The findings indicate scope for initiatives to identify and respond to gambling problems across a continuum of severity in treatment for affective disorders.
Since Costello’s (1972) seminal Behavior Therapy article on loss of reinforcers or reinforcer effectiveness in depression, the role of reward sensitivity and processing in both depression and bipolar disorder has become a central area of investigation. In this article, we review the evidence for a model of reward sensitivity in mood disorders, with unipolar depression characterized by reward hyposensitivity and bipolar disorders by reward hypersensitivity. We address whether aberrant reward sensitivity and processing are correlates of, mood-independent traits of, vulnerabilities for, and/or predictors of the course of depression and bipolar spectrum disorders, covering evidence from self-report, behavioral, neurophysiological, and neural levels of analysis. We conclude that substantial evidence documents that blunted reward sensitivity and processing are involved in unipolar depression and heightened reward sensitivity and processing are characteristic of hypomania/mania. We further conclude that aberrant reward sensitivity has a trait component, but more research is needed to clearly demonstrate that reward hyposensitivity and hypersensitivity are vulnerabilities for depression and bipolar disorder, respectively. Moreover, additional research is needed to determine whether bipolar depression is similar to unipolar depression and characterized by reward hyposensitivity, or whether like bipolar hypomania/mania, it involves reward hypersensitivity.