1. Down and Out in London: The relationship between Gambling and Homelessness
Steve Sharman
I will present data from 2 studies looking at the relationship between gambling and homelessness, and also present plans for a further piece of work
Not two things that might immediately spring to mind as being associated, but I’ll show you that the relationship is complex and that gambling is a important factor is homelessness

2. Homelessness: Defining and Quantifying
How is homelessness defined, and quantified?
-What is homelessness? Might seem like an obvious question, the stereotypical view of the tramp on a park bench – but it’s more than that
Talk about three types of homelessness

3. Rough Sleeping
Department for Communities and Local Government Autumn figures estimate that on any given night in Autumn 2015 approximately 3569 people were rough sleeping
30% increase on previous year; 102% increase from 2010
Largest regional increase seen in the South-West of England (41%)
Largest proportion of RS in London (26%), Westminster is the region with most RS
- RS is the most extreme for of homelessness, and is increasing
- These are only the ones that could be found, as RS stay out of view, therefore an accurate number is hard to estimate

4. Statutory Homelessness
Over 113,000 applications for statutory homelessness were made in the UK in 2015
The number of applications has been increasing annually since 2009
Applications more likely to be accepted if household has dependent children, pregnant member or considered vulnerable
Homelessness can also include living in temporary or sheltered accommodation
On 31 December 2015 there were 69,140 households in temporary accommodation, 12 per cent higher than at the same date in 2014.
Although not literally out on the streets, a household is considered homeless if they no longer have a legal right to occupy their accommodation or
if it would no longer be reasonable to continue to live there, for example, if living there would lead to violence against them
They can apply to their local authority for acceptance for housing assistance. (approx 50% accepted)
On this basis –

5. Hidden Homelessness
Individuals who are not captured by official figures are known as the ‘hidden homeless’
This includes those that are homeless, but are using temporary solutions such as squatting or sofa surfing
Research by Crisis indicates that about 62% of single homeless people are hidden and may not show up in official figures
Data collection focused on a survey of single homeless people carried out during one week in July 2010 in day centres in 11 towns and cities in England;

6. A vulnerable population
Homeless population show elevated levels of:
Drug and alcohol abuse (Wincup et al, 2003)
Mental illness (Scott, 1993)
Depression and Loneliness (Summerlin, 1995)
Instances of Childhood maltreatment (Torchalla et al, 2013)
However, very little research on levels of gambling in the homeless, using clinically validated tools
-Previous research in to HL show that when compared to the general public, HL show elevated levels of drug abuse, alcohol abuse, mental illness, depression, loneliness, and childhood maltreatment
-Despite apparent vulnerability to MH problems, very little research in the UK about gambling using proper tools

7. Gambling
The British Gambling Prevalence Survey 2010 found that 73% of UK adults have gambled in some form over the past 12 months (35.5 million people).
The BGPS used two tools to measure Problem Gambling prevalence. The PGSI (0.7%) and the DSM-IV screen 0.9% (Wardle et al, 2011).
BGPS in private households  overlooks various at risk populations, such as the homeless
-Gambling is hugely popular activity, enjoyed by millions in the UK and worldwide, and comes in many different forms
-In the UK, national level participation is looked at by the BGPS
-Most recent survey was 2010, showing 73% of population over the age of 16 had gambled in some form over the preceding 12 months , approx 35.5 million people
-How many gamble problematically? PGSI = 0.7%, DSM-IV = 0.9%, increase in previous years (0.6%, DSM, 2000 and 2007)
-Similar to other European countries, but lower than North America and territories like Hong Kong and Macau
-HOWEVER BGPS is only conducted in private households, therefore misses out high risk populations like halls of residence, prisons, and homeless.
- Could lead to an overall under-estimation of PG

8. Gambling and Homelessness
Previous research examining pathological gambling in homeless finds elevated levels:
Author
Year
Tool
Prevalence
Cohort
Nower et al
2014
SOGS
12%
275 afro-american males (US)
Shaffer et al
2002
MAGS
18.3%
171 seeking Tx for SUD (US)
Lepage et al
2000
17.2%
87 relying on community assistance (Canada)
-Some qualitative research in Aus and Holland, but all anecdotal, no prev rates
-Most previous prev research is in North America
-Shows increased rates, but difficult to compare as used different tools
-But nothing to show PG prev in homeless in UK – where we come in!

9. Study 1
To estimate gambling involvement, and problem gambling, in the homeless population in the UK.
The Problem Gambling Severity Index (Ferris & Wynne, 2001) was used to assess problem gambling prevalence with homeless individuals seeking help from Westminster council
-A largely descriptive study looking at PG in homeless – collaboration between Cam Uni, NPGC and Westminster local authority
-NPGC is the only NHS run gambling clinic in the UK, over 3000 PG’s since inception. Westminster have the highest number of RS in Westminster
-Through this collaboration, we were able to set up a study to address this gap in the literature
-Measured PG prev with PGSI to allow comparison with population level in BGPS

10. Data Collection
Participants were recruited from 16 homeless centres across Westminster (n= 456).
The centres from which participants were recruited included shelters, hostels and day centres.
Participants who answered yes to a screening question regarding gambling involvement completed the PGSI and provided further demographic data.
Participants who answered no to the screening question provided limited further data.
-16 Homeless centres across Westminster (Shelters, Day centres, hostels)
-Data collected by key workers, allowed a good sample size, they had good relationship already in replace
-Semi structured interviews

11. Results Overall sample (n=456):
PGSI ‘problem gambling’ (score > 7): 11.4%.
‘Moderate risk’ (score 5-7): 3.7%
‘Low risk’ (score 1-4): 8.3%
‘No risk’ (score 0): 76.6%.
No risk – 76.6%
Low risk – 8.3%
Mod Risk – 3.7%
PG – 11.4%
Compared to 0.7% in the general population

12. Homeless vs. BGPS
Group percentage for each of the 4 risk profiles from the entire sample. Reliable difference between the proportions in each group (χ² (3) = 11.1, p < .011).
Data for those who scored ≥1 on the PGSI, indicating some level of risk. Significantly greater proportion of at risk gamblers are problem gamblers relative to the BGPS data (χ²(2)=47.1, p < .001).
-In all categories: most obvious differences in No risk and PG: HL have less no risk, and more PG, important difference highlighted by the red circle in the graph on the left.
-significant overall difference
-graph 2: these are just the at risk gamblers (i.e. score of >0 on the PGSI). Shows the overall proportions in each risk category of the overall at risk population
-BGPS shows a downward stepwise pattern as gambling severity increases. Around 10% of the at risk pop in the BGPS are PG
-In the homeless, 50% of at risk gamblers are PG. second red circle highlights this.
-Big difference in the low risk gamblers, very low in homeless. quicker progression through risk categories

13. Sleeping Status and Preferred Form
Gambling risk distribution differs between hostel residents and rough sleepers (χ²(2)= 9.9, p= .007).
Game preferences (n=106): FOBTs and horse racing were the most popular gambling activities among homeless problem gamblers; online and casino gambling least common.
-when divided by sleeping status (RS v hostel res) another pattern occurs; again looking at the proporton of at risk gamblers, RS show a higher proportion of PG
-can imply that sleeping status effects likelihood of developing gambling problems
-preferred form: most pop were horses, FOBT, fruits and slots. Why? All found in bookies / arcades: open 24 hours, warm, dry, safe, low stake machines. A safe place where people can legitimately be.
-least common: casino (dress code, no fixed address for membership) and internet (no access, or restricted access in hostels)

14. Interim Summary
-Where next? Study 1 has shown elevated levels of PG in the homeless, but no direction of the effect what we really need to know is whether gambling is a behaviour that is a cause of homelessness, or if it is something that develops after becoming homeless

15. Study 2
Focus on replicating increased PG prevalence rates seen in Study 1
Cause or consequence?
Negative Life Events Scale to assess pathways into homelessness / gambling.
Questions on smoking, drug, and alcohol use to establish substance abuse prevalence and relationships to gambling
Awareness and use of gambling support services
-Second stage aims to replicate elevated levels of PG
-ask about negative life events, such as unemployment, drug and alcohol use, interaction with crime, to establish which events lead to homeless, and which happen as a consequence.
-also use DSM-IV criteria to examine levels of drug and alcohol use and abuse, and the relationship with gambling
-also measure awareness of and use of treatment services to give a more complete picture of how to help the homeless population

16. Data Collection

17. Results

18. Questions and References