ORI GIN AL PA PER

Rates of Problematic Gambling in a British HomelessSample: A Preliminary Study

Steve Sharman • Jenny Dreyer • Mike Aitken • Luke Clark •

Henrietta Bowden-Jones

� 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. ClarkDepartment of Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UKe-mail: sps48@cam.ac.uk

J. DreyerConnection @ St Martins, 12 Adelaide Street, London WC2N 4HW, UK

J. DreyerKings College, London, UK

M. AitkenDepartment of Psychology, Institute of Psychiatry, Kings College London, De Crespigny Park, LondonSE5 8AF, UK

H. Bowden-JonesNational Problem Gambling Clinic, Soho Centre for Health & Care, 1 Frith Street, London W1D 3HZ,UK

H. Bowden-JonesDepartment of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ,UK

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J Gambl StudDOI 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

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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 a reliability 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.

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To establish any difference between prevalence rates in the homeless compared to the

existing BGPS dataset, a Chi square (v2) 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 p value 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 (v2 (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 (v2 (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, (v2 (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

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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

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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

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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.

References

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.).Washington, DC: American Psychiatric Association.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.).Arlington, VA: American Psychiatric Publishing.

Bondolfi, G., Osiek, C., & Ferrero, F. (2008). Prevalence estimates of pathological gambling in Switzerland.Acta Psychiatrica Scandinavica, 117, 236–239.

Cox, B., Yu, N., Afiffi, T., & Ladouceur, R. A. (2005). National survey of gambling problems in Canada.Canadian Journal of Psychiatry, 50, 213–217.

Crane, M., Byrne, K., Fu, R., Lipmann, B., Mirabelli, F., Rota-Bartelink, A., et al. (2005). The causes ofhomelessness in later life: Findings from a 3-nation study. Journal of Gerontology Social Sciences,60B(3), S152–S159.

Currie, S. R., Casey, D. M., & Hodgins, D. C. (2010). Improving the psychometric properties of the problemgambling severity index. Report submitted to the Interprovincial Problem Gambling Research Consor-tium. Retrieved from http://www.ccgr.ca/wp-content/uploads/2013/03/Improving-the-Psychometric-Properties-of-the-Problem-Gambling-Severity-Index.pdf.

Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index user manual. Report to theCanadian inter-provincial task force on problem gambling. Canada: The Canadian Centre on Sub-stance Abuse.

Holdsworth, L., & Tiyce, M. (2012). Exploring the hidden nature of gambling problems among people whoare homeless. Australian Social Work, 65(4), 474–489.

Homelessness Statistics. Department for Communities and Local Government. Retrieved Aug 7th, 2013 fromhttps://www.gov.uk/government/organisations/department-for-communities-and-local-government/series/homelessness-statistics.

Lepage, C., Ladouceur, R., & Jacques, C. (2000). Prevalence of problem gambling among communityservice users. Community Mental Health Journal, 36(6), 597–601.

Lesieur, H., & Blume, S. (1987). The South oaks gambling screen (SOGS): A new instrument for theidentification of pathological gamblers. American Journal of Psychiatry, 144, 1184–1188.

Nower, L., Eyrich-Garg, K. M., Pollio, D. E., & North, C. S. (2014). Problem gambling and homelessness:Results from an epidemiologic study. Journal of Gambling Studies, 1–13.

Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling andother psychiatric disorders: Results from the national epidemiologic survey on alcohol and relatedconditions. Journal of Clinical Psychiatry, 66(5), 564–574.

Sadler, M. A. (2000). Escaping poverty: Risk-taking and endogenous inequality in a model of equilibriumgrowth. Review of Economic Dynamics, 3(4), 704–725.

Scott, J. (1993). Homelessness and mental illness. The British Journal of Psychiatry, 162(3), 314–324.Shaffer, H. J., Freed, C. R., & Healea, D. (2002). Gambling disorders among homeless persons with

substance use disorders seeking treatment at a community centre. Psychiatric Services, 53(9),1112–1117.

Shaffer, H. J., Hall, M. N., & Vander Bilt, J. (1999). Estimating the prevalence of disordered gamblingbehaviour in the United States and Canada: A research synthesis. American Journal of Public Health,89(9), 1369–1376.

Shaffer, H. J., LaBrie, R., Scanlan, K. M., et al. (1994). Pathological gambling among adolescents: Mas-sachusetts gambling screen (MAGS). Journal of Gambling Studies, 10, 339–362.

Street to Home Annual Report, 1st April 2011–31st March 2012. (2012). Retrieved Aug 7th, 2013, fromhttp://www.broadwaylondon.org/CHAIN/Reports/S2H_201112.pdf.

Sumerlin, J. R. (1995). Adaptation to homelessness: Self-actualization, loneliness, and depression in streethomeless men. Psychological Reports, 77(1), 295–314.

J Gambl Stud

123

van Laere, I. R., de Wit, M. A., & Klazinga, N. S. (2009). Pathways into homelessness: Recently homelessadults problems and service use before and after becoming homeless in Amsterdam. BMC PublicHealth, 9, 3.

Wardle, H., Moody, A., Spence, S., Orford, J., Volberg, R., Jotangia, D., et al. (2010). British GamblingPrevalence Survey 2010. London (UK): National Centre for Social Research.

Welte, J., Barnes, G., Wieczorek, W., Tidewell, M., & Parker, J. (2001). Alcohol and gambling pathologyamong US adults: Prevalence, demographics patterns and comorbidity. Journal of Studies on Alcohol,62, 706–712.

Wincup, E., Buckland, G., & Bayliss, R. (2003). Youth homelessness and substance use: Report to the drugsand alcohol research unit. London: Home Office.

J Gambl Stud

123