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Rates of Problematic Gambling in a British Homeless Sample: A Preliminary Study

DOI:10.1007/s10899-014-9444-7
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Steve Sharman at King's College London
Steve Sharman
Jenny Dreyer
Jenny Dreyer
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Mike Aitken
Mike Aitken
Mike Aitken
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Luke Clark at University of British Columbia
Luke Clark
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Abstract

Homelessness and problem gambling are two public health concerns in the UK that are rarely considered concurrently, and little is known about the extent of gambling involvement and problematic gambling in the homeless. We recruited 456 individuals attending homelessness services in London, UK. All participants completed a screen for gambling involvement, and where gambling involvement was endorsed, the Problem Gambling Severity Index (PGSI) was administered. The PGSI risk categories were compared against data from the 2010 British Gambling Prevalence Survey (BGPS). PGSI problem gambling was indicated in 11.6 % of the homeless population, compared to 0.7 % in the BGPS. Of participants endorsing any PGSI symptoms, a higher proportion of homeless participants were problem gamblers relative to the low and moderate risk groups, compared to the BGPS data. These results confirm that the homeless constitute a vulnerable population for problem gambling, and that diagnostic tools for gambling involvement should be integrated into homelessness services in the UK.
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ORIGINAL PAPER
Rates of Problematic Gambling in a British Homeless
Sample: A Preliminary Study
Steve Sharman •Jenny Dreyer •Mike Aitken •Luke Clark •
Henrietta Bowden-Jones
Published online: 23 January 2014
Springer Science+Business Media New York 2014
Abstract Homelessness and problem gambling are two public health concerns in the UK
that are rarely considered concurrently, and little is known about the extent of gambling
involvement and problematic gambling in the homeless. We recruited 456 individuals
attending homelessness services in London, UK. All participants completed a screen for
gambling involvement, and where gambling involvement was endorsed, the Problem
Gambling Severity Index (PGSI) was administered. The PGSI risk categories were com-
pared against data from the 2010 British Gambling Prevalence Survey (BGPS). PGSI
problem gambling was indicated in 11.6 % of the homeless population, compared to 0.7 %
in the BGPS. Of participants endorsing any PGSI symptoms, a higher proportion of
homeless participants were problem gamblers relative to the low and moderate risk groups,
compared to the BGPS data. These results confirm that the homeless constitute a vul-
nerable population for problem gambling, and that diagnostic tools for gambling
involvement should be integrated into homelessness services in the UK.
S. Sharman (&)M. Aitken L. Clark
Department of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK
e-mail: sps48@cam.ac.uk
J. Dreyer
Connection @ St Martins, 12 Adelaide Street, London WC2N 4HW, UK
J. Dreyer
Kings College, London, UK
M. Aitken
Department of Psychology, Institute of Psychiatry, Kings College London, De Crespigny Park, London
SE5 8AF, UK
H. Bowden-Jones
National Problem Gambling Clinic, Soho Centre for Health & Care, 1 Frith Street, London W1D 3HZ,
UK
H. Bowden-Jones
Department of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ,
UK
123
J Gambl Stud (2015) 31:525–532
DOI 10.1007/s10899-014-9444-7
Keywords Problem gambling Homelessness Prevalence UK London
Introduction
Problem gambling and homelessness are public health concerns that have a significant
impact on affected individuals, their families, and the wider society. Until recently,
problem gambling was categorized as an Impulse Control Disorder in the Diagnostic and
Statistical Manual Fourth Edition (DSM-IV, APA 1994). Based on shared characteristics
with substance use disorders, including symptom hallmarks like tolerance, craving and
withdrawal, the recent DSM-5 has reclassified gambling disorder into the ‘Addiction and
Related Disorders’ category (APA 2013). Existing research in the homeless has revealed
elevated levels of mental health problems (Scott 1993), including drug and alcohol use
disorders (Wincup et al. 2003), as well as depression and loneliness (Sumerlin 1995).
Despite this evident vulnerability of the homeless population to addictive disorders, little
research has been done to investigate the relationship between homelessness and gambling,
and indeed these issues are predominantly researched independently.
Within the general population, it is well-recognized that gambling is a popular leisure
activity. In the UK, the 2010 British Gambling Prevalence Survey (BGPS, Wardle et al.
2010) found that 73 % of adults reported gambling in some form over the past 12 months.
Using the Problem Gambling Severity Index (PGSI, Ferris and Wynne 2001) to quantify
disordered gambling, the BGPS indicated a prevalence rate of 0.7 %, which is comparable
to rates in other countries in Europe (e.g. Switzerland, 0.8 %, Bondolfi et al. 2008) and
slightly lower than prevalence rates suggested for North America (US, 1.6 %, Shaffer et al.
1999; Canada, 2.0 %, Cox et al. 2005).
Ascertaining levels of homelessness is a difficult task due to the transient nature of the
homeless population, combined with the fact that many homeless people strive to stay
anonymous. Homelessness can constitute either living in temporary accommodation, or
‘sleeping rough’ on the streets (the government definition of rough sleepers is ‘people
sleeping…or bedded down in the open air…or other places not designed for habitation’,
(Department for Communities and Local Government 2012). Council figures for Central
London in the year starting April 2011 recorded 5,678 rough sleepers (12 % female) in
contact with outreach workers (Street to Home Annual Report 2012), with around half of
these in the Westminster authority in Central London where the current study was con-
ducted. These figures represented a 43 % increase from the previous year (2010–2011),
indicating that this is not only a significant social problem in London, but one that appears
to be increasing.
A small number of previous international studies have examined levels of gambling and
problematic gambling in the homeless. Using the Massachusetts Gambling Screen (based
on the DSM-IV criteria), Shaffer et al. (2002) studied 171 homeless people who were
seeking treatment for substance use disorders. ‘At-risk’ gambling was present in 18.3 %,
and pathological gambling in 5.5 %. Two studies using the South Oaks Gambling Screen
(SOGS, Lesieur and Bloom 1987) reported a prevalence rate of probable pathological
gambling of 17.2 % in 87 individuals relying on community assistance in Canada (LePage
et al. 2000), and 23 % in 275 homeless people in the USA (Nower et al. 2014). Qualitative
studies have also provided some insight on the causal connection here, that gambling
represents a contributory factor for some individuals becoming homeless (Holdsworth and
526 J Gambl Stud (2015) 31:525–532
123
Tiyce 2012; van Laere et al. 2009). For example, gambling was listed in the top ten
contributing factors for homelessness in older adults surveyed across the US, Australia and
UK (Crane et al. 2005).
The present study represented the first attempt to measure levels of gambling
involvement and problem gambling in homeless individuals accessing services in the UK.
We investigated the distribution of gambling involvement as indicated by PGSI risk cat-
egories. Secondary aims were to characterize any gender differences, associations with
current housing circumstances (i.e. temporary accommodation versus rough sleeping), and
the different preferred forms of gambling engaged in by the homeless.
Methods
Participants
Participants were recruited from 16 homeless centres across Westminster, London in
January 2012 (n=456). The centres from which participants were recruited included
shelters, hostels and day centres. From the overall sample, 160 participants provided age
(m=41.9, SD 11.9, range 18–78), 264 participants provided gender information (246
male, 18 female) and 291 provided current housing circumstance (206 Hostel Residents, 83
Rough sleepers, 2 squatters). The recruitment and study protocol was given ethical
approval by Kings College London. The study was also approved by the commissioning
manager of Westminster City Council’s Rough Sleeping team. Participants were informed
of the nature of the study, and provided verbal consent.
Problem Gambling Severity Index (PGSI, Ferris and Wynne 2001)
The PGSI is a 9 item questionnaire measuring gambling severity, derived from the longer
Canadian Problem Gambling Index. Each item is scored from 0 to 3 (never =0, some-
times =1, most of the time =2, almost always =3), resulting in a total score range of
0–27. Cronbach’s areliability coefficient was 0.95 in the present study, indicating that this
is a reliable scale to use on this population. The gambling risk categories were based upon
Currie et al. (2010): a score of 0 indicates a non-problem gambler, scores of 1–4 indicate a
‘low risk’ gambler, 5–7 indicate a ‘moderate risk’ gambler and a score of [7 indicates a
problem gambler.
Data Collection and Analysis
To allow comparison of prevalence rates with the BGPS, a full PGSI was administered to
participants who scored C1 on item one of the PGSI (‘‘In the last 12 months, have you bet
more than you could afford to lose?’’). All participants (n=457) completed this item as a
screening question, with 363 (79.6 %) scoring zero by answering ‘‘0—never’’ to this item.
At some study sites, participants who answered ‘never’ to item one were nonetheless
administered the full PGSI (n=147). Of these, 135 participants (91.8 %) scored zero. In
the remaining 12 individuals who answered never to item one, but scored [0 on the full
scale, 6 scored in the low risk category, six in the moderate risk category, and none scored
in the problem gambler category.
J Gambl Stud (2015) 31:525–532 527
123
To establish any difference between prevalence rates in the homeless compared to the
existing BGPS dataset, a Chi square (v
2
) analysis was conducted. The distribution of
gamblers scoring C1 amongst risk profiles within the homeless and the BGPS dataset was
also analysed using a Chi square analysis. A pvalue of \.05 was considered significant.
Results
Participant’s scores on the PGSI were classified as no risk (n=350), low risk (n=38),
moderate risk (n=15) or problem gamblers (n=53) based on the published thresholds.
In the overall homelessness sample (n=456), the rate of problem gambling on the
PGSI was 11.6 %, with moderate risk gambling in 3.3 %, low risk gambling in 8.3, and
76.8 % registering no risk (i.e. PGSI =0) (see Fig. 1a).
In subjects who confirmed their gender, males (n=246) had a problem gambling rate
of 20.8 %, a moderate risk rate of 5.6 %, a low risk rate of 14.2 % and no risk rate of
59.4 %. In the female participants (n=18), the problem gambling rate was 5.5 %, the
moderate risk rate was also 5.5 %, the low risk rate was 11.1 % and the no risk rate was
77.8 %. Due to the very low number of confirmed female participants, gender differences
are not discussed further.
Comparing the proportions of the overall sample falling in the different gambling risk
groups against the general UK population from the BGPS, there was a reliable difference
between two datasets (v
2
(3) =11.1, p\.011), with the largest differences in the no risk
and problem gambler groups. Removing the no risk group, and analysing only participants
with PGSI scores C1, there was a reliable difference in the proportions of participants in
the ‘at risk’ categories between the homeless dataset and the BGPS data (v
2
(2) =47.1,
p\.001). The BGPS data indicates a stepwise decline in prevalence as gambling severity
increases (i.e. moving from low risk to problem gambling). In contrast, in the homeless
sample, there was a significantly greater proportion of problem gamblers relative to the low
risk and moderate risk categories (see Fig. 1b).
Further analysis looked at the gambling risk categories as a function of current housing
circumstance (see Fig. 2a). There was a significant difference in risk profiles between
Hostel Residents and Rough Sleepers, (v
2
(2) =9.9, p=.007): hostel residents displayed
a larger proportion of low risk gambling, whereas the rough sleepers displayed a higher
rate of problem gambling (Fig. 2a). In 106 participants who indicated game preferences,
electronic roulette machines and horse racing were the most popular gambling activities;
online and casino gambling were the least common (Fig. 2b).
Discussion
In a convenience sample of service-accessing homeless individuals attending outreach
centres in Central London, UK, the rate of problem gambling detected using the PGSI was
11.6 %, which is substantially higher than the general population figures for the UK
indicated by the BGPS. A second finding is that the distribution of PGSI risk categories
differed markedly in our homeless sample relative to the BGPS data. While the BGPS data
show the expected profile of decreasing prevalence with greater gambling severity, of those
individuals who scored C1 on the PGSI, the proportion of problem gamblers was sub-
stantially higher in the homeless sample, and the proportion of low risk gamblers was
substantially lower. This high rate of problem gambling was evident in our homeless
528 J Gambl Stud (2015) 31:525–532
123
participants who self-reported male gender, and it was particularly evident in the rough
sleepers. Analysis of gambling type demonstrated that shop-based gambling activities
including electronic roulette (on a ‘Fixed Odds Betting Terminal’), slot machines and
sports/horse betting, were the most common forms of betting among the homeless problem
gamblers.
These observed rates of problem gambling in the homeless are similar to past studies
from North America (LePage et al. 2000, 17.2 %; Nower 2014, 23 %; Shaffer et al. 2002,
5.5 %). It is possible that our detected rate is a conservative estimate due to the imple-
mentation of our screening question, which assumed an overall PGSI score of zero for
participants who did not endorse the first item on the PGSI. This assumption was supported
by an analysis in a subset of our sample (n=147) who did not endorse the screening item
but nevertheless completed the full PGSI. While 92 % of these participants did score zero
on the full scale, 12 participants manifested some level of problematic gambling, and thus
our screening question may have slightly under-estimated the overall prevalence rate. The
instrument for assessing problematic gambling also differs across studies; the two studies
demonstrating the highest prevalence rates (LePage et al., Nower et al.) used the SOGS,
Fig. 1 PGSI risk profile including and excluding ‘No Risk’ category
Fig. 2 Risk profile per housing status and gambling activity
J Gambl Stud (2015) 31:525–532 529
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Shaffer et al. used the MAGS, and our study used the PGSI. It is possible that these
different screens may capture distinct facets of problematic gambling amongst the
homeless; cross-screen tool validation in this population has yet to be conducted.
Despite the possibility of a conservative estimate, the prevalence figure for problem
gambling in the homeless of 11.6 % is dramatically higher than the population prevalence
estimate from the BGPS. It should be acknowledged that the BGPS data were collected by
post, resulting in the exclusion of a number of vulnerable populations, including the
homeless (also the prison population and student halls of residence). Thus, the BPGS
prevalence figure is itself likely to represent an overly conservative estimate of problem
gambling in the general population. One caveat to this comparison is that the BGPS data
were collected prior to 2010, and our data were collected in 2012, and therefore it is
possible that national gambling involvement may have fluctuated over this time; however
the increase in problem gambling rates observed between previous versions of the BPGS
(from 1999 to 2007, and from 2007 to 2010) are minimal, and unlikely to influence these
results.
The elevated prevalence of problem gambling was particularly apparent in males.
However, the sampling strategy did not attain equal representation of both genders, and a
very small number of homeless women participated, precluding statistical analysis by
gender. The heavily male-weighted sample could also bias the analysis of preferred
gambling forms, as the most popular activities were those found in bookmakers’ shops,
which are traditionally male-dominated environments. Within the subgroup of the home-
less who reported at least some level of gambling, the rate of problem gambling was
particularly elevated in the rough sleepers compared to hostel residents. One possible link
between sleeping status, gambling and gambling type may be the shelter offered by high
street gambling venues in the UK. We estimated that there were 61 such venues in the
immediate vicinity of our outreach centres. Amusement arcades and bookmaker’s shops
can have lengthy opening hours, with typical opening times from 8 a.m. to 11 p.m., and
some open 24 h. High street amusement arcades also offer very low stake gambling, from
as little as 5 p per play, and some offer free hot drinks and snacks. Extended exposure to
such an environment may increase risk of problem gambling in the homeless. Consistent
with this notion, the most common forms of gambling among our cohort were those offered
by bookmakers (roulette machines, sports betting).
The observed increased rate of problem gambling amongst the homeless population
highlights the relationship between poverty and financial risk taking. When faced with
poverty, an individual may display risky behaviour in an effort to exit poverty (Sadler
2000). In the case of homelessness, the experienced level of poverty is extremely severe.
Nevertheless, our data do not allow any conclusions to be drawn regarding the directional
causality, as to whether problem gambling is a cause or a consequence of homelessness.
We also note that our sample was self-selecting, in that we were only able to recruit
individuals who accessed services provided by Westminster Local Authority.
Conclusions
This is the first study to use a clinically recognized diagnostic tool to show a significantly
higher rate of problem gambling in a service-accessing homeless population compared to
the general population in the UK. We observed a markedly higher proportion of problem
gamblers compared to low-risk gamblers in the homeless. Our findings confirm that
homeless people constitute a vulnerable population for excessive gambling, and imply that
530 J Gambl Stud (2015) 31:525–532
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the problems of homelessness and problem gambling may benefit from being addressed
concurrently rather than independently. Homelessness services should consider including
questions about gambling behavior in their support pathways, to enable homeless indi-
viduals to better access treatment.
Conflict of interest The authors declare that there is no conflict of interest in this study.
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This study examined associations between recreational gambling (RG) and at-risk/problem gambling (ARPG), and clinical measures of mental illness and substance use, functionality, homelessness, in a nationally representative sample of U.S. military veterans. Data were analyzed from 781 veterans who participated National Veteran Homeless and Other Poverty Experiences (NV-HOPE) study conducted in 2021. Chi-square tests, analyses of variance (ANOVAs) and logistic regressions analyses were conducted to examine unadjusted and adjusted associations between gambling group status and sociodemographic, psychiatric, substance use, functioning, and homelessness measures. A significant minority of low-income U.S. veterans reported gambling, with 24.9% (95% confidence interval [CI] 95% CI 21.12–28.76%) exhibiting RG and 6.7% (95% CI 3.88–9.42%) screening positive for ARPG. The prevalence of ARPG was higher among younger, non-White veterans. ARPG was associated with greater symptoms of substance use and anxiety; poorer physical functioning; history of any mental illness; lifetime history of homelessness; and having any student or car loans relative to NG. Veterans who had RG were more likely to screen positive for drug use disorders relative to NG. Results of the current study provide an up-to-date estimate of the current prevalence of RG and ARPG among low-income U.S. veterans and underscore the importance of routine screening, monitoring, and development of interventions for problematic gambling severity, as well as interventions for ARPG in this population. These findings may inform targeted intervention strategies for this vulnerable subpopulation.
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... The paucity of data in our survey on individuals experiencing disordered gambling re ects the relatively small proportion of the population falling into this category, but also that population surveys cannot comprehensively capture relatively rare behaviours -like disordered gambling [33], or injecting drug use [34] -which are more common in subgroups of the population who fall outside of traditional sampling frames [35]. Nonetheless, because they are more numerous, the majority of harms from gambling at the population-level is driven by those classi ed as low, and moderate risk of harm [36]. ...
The Intersection of Gambling with Smoking and Alcohol use in Great Britain: A Cross-Sectional Survey in October 2022
Preprint
Full-text available
  • Sep 2023
Objectives Gambling is often associated with cigarette smoking and alcohol consumption. This study aimed to explore the intersection of gambling across all risk levels harm with smoking and alcohol use among adults in Great Britain. Design A nationally representative cross-sectional survey in October 2022. Setting Great Britain. Participants A weighted total of 2,398 adults (18 + years). Outcome measures We examined the prevalence of gambling in the past year and, among those reporting gambling, assessed the associations between any risk of harm from gambling (scores of > 0 on the problem gambling severity index) and cigarette smoking and higher risk alcohol use. We also explored the average weekly expenditure on gambling, smoking and alcohol use among those categorised at any-risk of harm from gambling. Results Overall, 43.6% (95% CI 41.2–45.9) of adults gambled in the past year. Among those who gambled in the past year 7.3% (5.3–9.3) were classified at any-risk of harm from gambling, 16.0% 13.2–18.8) were currently smoking, and 40.8% (37.2–44.4) were drinking at increasing and higher risk levels. There were no apparent associations between any risk of harm from gambling and current cigarette smoking (ORadj = 0.80, 95% CI 0.35–1.66) or drinking at increasing and higher risk levels (ORadj = 0.94, 0.52–1.69), respectively. Analyses using Bayes factors indicated that these data were insensitive to distinguish no effect from a range of associations (OR = 0.5–1.9). The mean weekly spend on gambling was £7.69 (95% CI 5.17–10.21) overall, and £45.68 (12.07–79.29) among those at any risk of harm from gambling. Conclusions Pilot data in a population-level survey on smoking and alcohol use yielded estimates of gambling participation and at-risk gambling that are similar to other population-level surveys. Further data are needed to elucidate the intersections more reliably between gambling, smoking and alcohol use, and inform population-level approaches to reduce harms conferred by these behaviors.
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... It is also vitally important to recognize that autonomous decision-making systems can inadvertently reinforce and amplify existing biases, deepening social inequity (Benjamin, 2019). Given the established links between gambling problems and socioeconomic disadvantage (Sharman et al., 2015), future research in this area must seek to develop systems with both: (1) a high standard of overall classification performance, and (2) a high standard of individual classification performance. To ensure that machine learning systems for gambling harm detection do not further entrench social inequity, strategies for mitigating bias must be investigated (e.g. by exploring equal sensitivity and equalized odds corrections; Rajkomar et al., 2018). ...
Enabling New Strategies to Prevent Problematic Online Gambling: A Machine Learning Approach for Identifying At-risk Online Gamblers in France
Article
  • Feb 2023
Gambling activities are rapidly migrating online. Algorithms that effectively detect at-risk users could improve the prevention of online gambling-related harms. We sought to identify machine learning algorithms capable of detecting self-reported gambling problems using demographic and behavioral data. Online gamblers were recruited from all licensed online gambling platforms in France by the French Online Gambling Regulatory Authority (ARJEL). Participants completed the Problem Gambling Severity Index (PGSI), and these data were merged and synchronized with past-year online gambling behaviors recorded on the operators’ websites. Among all participants (N = 9,306), some users reported betting exclusively on sports (N = 1,183), horseracing (N = 1,711), or poker (N = 2,442) activities. In terms of Area Under the Receiver Operating Characteristic Curve (AUC), our algorithms showed excellent performance in classifying individuals at a moderate-to-high (PGSI 5+; AUC = 83.20%), or high (PGSI 8+; AUC = 87.70%) risk for experiencing gambling-related harms. Further, these models identified novel behavioral markers of harmful online gambling for future research. We conclude that machine learning can be used to detect online gamblers at-risk for experiencing gambling problems. Using algorithms like these, operators and regulators can develop targeted harm prevention and referral-to-treatment initiatives for at-risk users.
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... While the DSM-5 (APA, 2013) refers to gambling as 'disordered gambling' or 'gambling disorder' this article adopts the term problem gambling to align to the PGSI. Problem gambling has been linked to mental health problems (Lorains et al., 2011;Roberts et al., 2017), financial difficulties (Cowlishaw & Kessler, 2016;Grant et al., 2010), homelessness (Sharman et al., 2015), suicide (Ledgerwood & Petry, 2004), alcohol use disorders (Petry, 2005;Welte et al., 2001) and other problematic substance use (Petry, 2005(Petry, , 2007. ...
Gambling and crime: An exploration of gambling availability and culture in an English prison
Article
Full-text available
  • Nov 2022
Background: There is evidence that prisoners have the highest rate of problem gambling in any population, but little is known about the nature of in-prison gambling, the motives for it or how it relates to prior gambling behaviour. Aims: To investigate the prevalence and type of gambling prior to prison and the prevalence, type, and reasons for gambling in prison. Methods: Two hundred and eighty-two male volunteers in a Category B male prison in England completed a questionnaire which included the Problem Gambling Severity Index (PGSI). Results: One hundred and twenty-six (45%) reported gambling in prison, with eighty-one (30%) of participants reporting that gambling was a normal part of prison life. Pre-prison behaviour, whether type of index offence or prior gambling, had little relationship to in-prison gambling. Frequency of gambling in prison increased with increasing PGSI risk category. The most common types of gambling in prison were card/dice games, sports and ball games, while the most common motives were entertainment, excitement or sense of challenge and to win prizes, with significant differences in motive between PGSI risk categories. Prison canteen items formed the most common currency gambled. People within the higher PGSI risk category were more likely to have borrowed items from other prisoners. Conclusions: Our research has added to existing literature by identifying high rates of gambling in prison and showing that prisoners' perceptions of gambling are as a normal part of prison life. Findings suggest that screening and support should be available to manage gambling in prison, including support to reduce gambling-related debt, particularly given associations between debt and violence in prison. Relief from boredom and need for excitement were among the most common reasons for gambling in prison, indicating that there is a need to provide a more appropriately stimulating prison environment.
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... Compared with studies in other countries such as Canada and the United States, where lifetime prevalence was reported to be 10% (Matheson et al., 2014) and 12.0% (Nower et al., 2015), respectively, the lifetime prevalence among homeless people in our study was especially high. However, fewer Japanese were identified as "high-risk gamblers" in the past year using the PGSI (7.8%) than in the United Kingdom-at 11.6% (Sharman et al., 2015) and 23.6% (Sharman et al., 2016)-and Poland at 11.3% (Wieczorek et al., 2019). These differences in problem gambling prevalence rates among homeless populations may result from several factors, including the prevalence of GD in each country; cultural context of gambling; definition and characteristics of the homeless subgroups; and differences in sampling methods, response rates, scales used, and order of screens (Griffiths, 2015;Stevens & Young, 2008;Williams et al., 2012). ...
Prevalence of gambling disorder and its correlates among homeless men in Osaka city, Japan
Article
Full-text available
Internationally, the prevalence of gambling disorder has been reported to be higher among homeless people than the general population; however, little is known about the factors associated with gambling disorder in this population. The present study aimed to investigate the prevalence of gambling disorder and its associated factors among homeless men using shelters in Osaka City. A cross-sectional survey was conducted from 30 to 2018 to 4 January 2019, using the 2017 Japanese National Survey of Gambling (JNSG) questionnaire, supplemented with questions about homeless experiences, drinking, and smoking. Using the South Oaks Gambling Screen, the presence of gambling disorder was determined by a score ≥ 5 out of 20. Multivariate logistic regression was conducted to explore factors associated with lifetime gambling disorder. Lifetime and past-year prevalence of gambling disorder among 103 participants was 43.7% (95% confidence interval [CI]: 34.5–53.3) and 3.9% (95% CI: 1.5–9.6), respectively, which are higher than the 6.7% and 1.5% found among men in the 2017 JNSG. Reasons reported for currently gambling less were primarily financial. Factors associated with lifetime GD included “more than 20 years since the first incidence of homelessness” (adjusted odds ratio [AOR]: 4.97, 95% CI: 1.50–16.45) and “more than five incidences of homelessness” (AOR: 4.51, 95% CI: 1.06–19.26). When homeless individuals with gambling disorder try to rebuild and stabilize their lives, the presence or resurgence of gambling disorder may hinder the process and pose a risk of recurring homelessness. Comprehensive support services for homeless individuals with gambling disorder are required. (250 words)
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... For example, both surveys exclude persons who do not have a residential address, such as those who are experiencing homelessness, and also fails to include people who reside at institutional addresses such as hospitals, prisons, military barracks, and student halls of residence. Such populations are likely to have higher rates of gambling problems [4,5]. As a consequence, both surveys are likely to significantly underreport gambling related harm. ...
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Gambling and homelessness in older adults: A qualitative investigation
Article
  • Nov 2021
Background and Aims Homelessness is one of the most significant harms associated with gambling and appears to affect older adults disproportionately, but the relationship has received little research attention. This exploratory study investigated how gambling and homelessness is linked in older adults. Methods Using qualitative research methods, we undertook in-depth semi structured face-to-face individual and group interviews to gather data from a purposive sample (n=48) of key informants working in service provision for older adults (aged 50+ years) experiencing gambling related harm and/or homelessness in Victoria, Australia. Thematic analysis of data focused on evaluating mechanisms and identifying contextual conditions that activate pathways between gambling and homelessness. Results The relationship between gambling and homelessness in older adults is often indirect and non-linear, and can represent a reflexive cycle. Experiencing periods of homelessness into older age can contribute to gambling, often because the adverse impacts of homelessness on older adult’s mental and material wellbeing increase the appeal of gambling. Additionally, comorbidities (e.g. substance use, mental illness, past trauma) and structural conditions (e.g. gambling accessibility, poverty, housing insecurity) can activate gambling. Furthermore, because gambling in the older homeless adult population is frequently hidden, and regularly overlooked by service providers, it often continues unabated. Gambling in older adults can also contribute to the onset of first-time homelessness. Large and rapid losses from high-intensity gambling frequently characterise this route to homelessness. Such gambling is often triggered by major life events and changes (e.g. bereavement, job loss, relationship difficulties), and the outcomes are often worsened by the conduct of gambling operators and creditors. Conclusions The link between gambling and homelessness in older adults is complex, with connecting mechanisms often contingent on individual, interpersonal and structural conditions and contexts. There is potential for preventative and ameliorative action given many of the underlying conditions appear modifiable through policy intervention.
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Problem Gambling and Homelessness: Results from an Epidemiologic Study
Article
Full-text available
The purpose of this study was to investigate the prevalence of gambling disorder and comorbid psychiatric disorders in a homeless population and identify features related to potential subtypes. At baseline, participants were administered a structured interview including socio-demographic sections of the National Comorbidity Study (NCS) interview; seven diagnostic sections of the Diagnostic Interview Schedule (DIS); the alcohol and drug abuse sections of the Composite International Diagnostic Interview-Substance Abuse Module (CIDI-SAM); and the Homeless Supplement to the DIS. At nine months post-baseline assessment, participants were administered additional NCS family history questions and the South Oaks Gambling Screen (SOGS). Participants were an epidemiologic sample of 275 predominately African-American homeless individuals, grouped as lifetime non-gamblers (n = 60), recreational gamblers (n = 152), and problem gamblers (n = 63), recruited on the street and through homeless shelters. Results indicate that lifetime rates of sub-clinical problem (46.2 %) and disordered (12.0 %) gambling were significantly higher than in the general population. Problem gamblers were more likely than non-problem gamblers to meet diagnostic criteria for antisocial personality disorder, post-traumatic stress disorder, bipolar disorder, and any psychiatric disorder, and more likely than non-gamblers to use illicit drugs or meet criteria for abuse/dependence for nicotine, alcohol, or any substance. This study provides evidence that problem gambling is a significant public health issue among the African-American homeless population. Homeless services should include assessment for problem gambling along with psychiatric disorders and referrals to resources and treatment programs. Future studies should explore the relationship of the onset and course of problem gambling and other psychiatric disorders with homelessness as well as racial differences in gambling patterns and problem severity over time.
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Pathological Gambling among adolescents: Massachusetts Gambling Screen (MAGS)
Article
Full-text available
  • Dec 1994
This article describes the development of the Massachusetts Gambling Screen (MAGS). The purpose of the MAGS is to provide a brief clinical screening instrument that can (1) yield an index of non-pathological and pathological gambling during a 5 to 10 minute survey or interview and (2) document the first psychometric translation of the proposed DSM-IV pathological gambling criteria into a set of survey or clinical interview questions. The development data for this instrument were obtained from a survey of 856 adolescents who were students in suburban Boston high schools. The results provided evidence that weighted item scores (i.e., discriminant function coefficients) could correctly classify 96% of adolescent gamblers as pathological, in transition or non-pathological when DSM-IV criteria were employed as the conceptual referent. The results also describe the prevalence of a variety of social and emotional problems associated with adolescent gambling. Finally, the discussion examined the normalization and contemporary social context of gaming and the impact of these influences on the measurement and identification of pathological gambling.
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Exploring the Hidden Nature of Gambling Problems among People Who Are Homeless
Article
Full-text available
Although people with gambling problems are now recognised to be among those groups of people at increased risk of homelessness, little research has explored their experiences. This qualitative interpretive study explored the experiences of people who were homeless and had gambling problems, and the housing and gambling service providers assisting them. In-depth interviews were conducted with 17 service users and 18 service providers. A key finding was that gambling problems among those experiencing homelessness are often hidden; few people presented to housing services admitting to gambling problems. Shame, stigma, and identity issues were described as the main reasons service users did not disclose their gambling activities. The research highlighted that the relationship between service providers and service users was infused with power imbalances and shaped by social discourses and policies that demand self-responsibility and hinder information sharing between service providers and service users.
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Pathways into homelessness: Recently homeless adults problems and service use before and after becoming homeless in Amsterdam
Article
Full-text available
To improve homelessness prevention practice, we met with recently homeless adults, to explore their pathways into homelessness, problems and service use, before and after becoming homeless. Recently homeless adults (last housing lost up to two years ago and legally staying in the Netherlands) were sampled in the streets, day centres and overnight shelters in Amsterdam. In April and May 2004, students conducted interviews and collected data on demographics, self reported pathways into homelessness, social and medical problems, and service use, before and after becoming homeless. among 120 recently homeless adults, (male 88%, Dutch 50%, average age 38 years, mean duration of homelessness 23 weeks), the main reported pathways into homelessness were evictions 38%, relationship problems 35%, prison 6% and other reasons 22%. Compared to the relationship group, the eviction group was slightly older (average age 39.6 versus 35.5 years; p = 0.08), belonged more often to a migrant group (p = 0.025), and reported more living single (p < 0,001), more financial debts (p = 0.009), more alcohol problems (p = 0.048) and more contacts with debt control services (p = 0.009). The relationship group reported more domestic conflicts (p < 0.001) and tended to report more drug (cocaine) problems. Before homelessness, in the total group, contacts with any social service were 38% and with any medical service 27%. Despite these contacts they did not keep their house. During homelessness only contacts with social work and benefit agencies increased, contacts with medical services remained low. the recently homeless fit the overall profile of the homeless population in Amsterdam: single (Dutch) men, around 40 years, with a mix of financial debts, addiction, mental and/or physical health problems. Contacts with services were fragmented and did not prevent homelessness. For homelessness prevention, systematic and outreach social medical care before and during homelessness should be provided.
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The South Oaks Gambling Screen (SOGS): A New Instrument for the Identification of Pathological Gamblers
Article
Full-text available
  • Oct 1987
The South Oaks Gambling Screen is a 20-item questionnaire based on DSM-III criteria for pathological gambling. It may be self-administered or administered by nonprofessional or professional interviewers. A total of 1,616 subjects were involved in its development: 867 patients with diagnoses of substance abuse and pathological gambling, 213 members of Gamblers Anonymous, 384 university students, and 152 hospital employees. Independent validation by family members and counselors was obtained for the calibration sample, and internal consistency and test-retest reliability were established. The instrument correlates well with the criteria of the revised version of DSM-III (DSM-III-R). It offers a convenient means to screen clinical populations of alcoholics and drug abusers, as well as general populations, for pathological gambling.
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Escaping Poverty: Risk-Taking and Endogenous Inequality in a Model of Equilibrium Growth
Article
  • Feb 2000
Recent macroeconomic models of income distribution generate equilibria characterized as poverty traps. These models specify a production indivisibility such that, due to problems of asymmetric information in credit or capital markets, poor agents are never able to acquire the resources necessary to overcome the indivisibility. In the context of an equilibrium growth model, this paper demonstrates that faced with such constraints, poor, risk-averse agents have incentives to voluntarily take on risk in the hopes of exiting poverty. Each period they remain in poverty, they sacrifice a small amount of current consumption to pool resources for this risky activity. Risk-taking, economic mobility, and the distribution of income are generated endogenously. Furthermore, beginning from identical endowments, "initial" inequality also emerges endogenously. It is shown that voluntary risk-taking eliminates many potential steady-state equilibria of this and other models -- those that exhibit individual poverty traps. All agents (or dynasties) expect to escape at some future date, after a perhaps extended spell in poverty. (Copyright: Elsevier)
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