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Page 1
The road less travelled… a longitudinal study
Rosa Billi,
Manager, Research
European Association of Gambling Studies
Helsinki, Finland, September 2014
Where is Victoria?
Victorian Gambling Study
Page 3
Presentation Overview
Background
Study design
Findings
Challenges
The future
Acknowledgements
Page 4
The road more travelled....
•Most gambling epidemiology studies are cross sectional and/or retrospective
•Assess current participation and problems (prevalence)
•Give an indication of past participation and problems
•Provide information on distribution and potential risk/protective factors
•Additional information, e.g. help-seeking
•Frequently methodologically compromised
•Limitations - temporal sequence, causal inference, risk/protective factors for problem onset and progression
•Prevalence study 'replications' provide estimates of change over time (snapshots at population level)
Page 5
Travelling the road…
A wider variety of research methods, including longitudinal studies…. would broaden our base of knowledge about gambling and problem gambling…
Abbott and Volberg (1996)
The road less travelled…moving from distribution to determinants in the study of gambling epidemiology.
Shaffer et al (2004)
The hope that this road will become ‘more’ travelled and in the process help shift the focus of gambling studies from gambling distribution to gambling determinants.
Abbott and Clarke (2007)
Page 6
Early journeys along the road
Early longitudinal studies - shortcomings:
•Usually Clinical populations
•Short time spans
•Mix of 'add-ons' and stand alone studies
•Mostly small/moderate samples not representative of general population
•Various methodological problems including high attrition
•Psychological focus
Volberg (2010) el-Guebaly et. al (2008)
Prospective (longitudinal) important:
•Relatively little knowledge re people who report problems at a particular point in time (prevalence) and whether these will resolve over time- stability of condition
•Provides insights into incidence or the number of new cases that develop over time
Delfabbfro (2013)
Page 7
Background
Objectives to explore:
• Risks and vulnerabilities related to changes in gambling status
• Incidence
• Movements in and out of PGSI states
Eleven hypotheses (including)
• Gamblers move in and out of PGSI states
• Problem gambling is transitory in nature
• Co morbidities are clustered together
• Chasing wins to cover losses is the biggest predictor of problem gambling
• Contextual factors contribute to problem gambling
• EGMs and other continuous forms of play are more likely to result in problem
gambling than non continuous forms of play
• Gamblers with moderately high PGSI scores are more likely to transition to
problem gambling.
Page 8
Design
Cross Sectional (W1)
15,000 RDD CATI survey
70/20/10
representative of Vic population
Prospective cohort or longitudinal (W2, W3, W4)
annual follow up x 3
Qualitative component
face to face interviews (n=44)
Design
Map of Victorian Government Regional Boundaries 2008
Wave One July 2008 - October 2008
Wave Two September 2009 - January 2010
Wave Three September 2010 - January 2011
Qualitative May 2011 - August 2011
Wave Four October 2011 - January 2012
Data collection periods
Page 11
Sample
Page 12
•Gambling participation in 12 activities:
– informal private betting; electronic gaming machines (EGMs); table games (e.g,
blackjack, roulette, poker); horse or harness racing or greyhounds; sports and event results; Lotto, Powerball or the Pools; Keno; scratch tickets; bingo; telephone or SMS competitions; raffles, sweeps and other competitions; and speculative stock investments.
•Gambling behaviour using the Problem Gambling Screening Index (PGSI):
– Nine-item index with scores from 0 to 27
– Non-gambler, non-problem gambler (PGSI=0), low-risk gambler (PGSI=1-2), moderate-risk gambler (PGSI=3-7), problem gambler (PGSI=8-27)
•Lifetime risk of gambling using NORC DSM-IV Screen for Gambling Problems – Control, Lying and Preoccupation (NODS-CLiP2) scale:
– Lifetime non-problem gambler (NODS=0); lifetime at-risk gambler (NODS=1,2);
lifetime problem gambler (NODS=3-4); lifetime pathological gambler (NODS≥5)
Gambling participation questions
PGSI and NODS CLiP2
PGSI
•Measures past year risk
•Non gambler 0
•Non problem gambler 0
•Low risk gambler 1-2
•Moderate risk gambler 3-7
•Problem gambler 8+
NODS CLiP2 •Measures lifetime risk
•Lifetime non problem 0
•Lifetime at risk1-2
•Lifetime problem gambler 3-4
•Lifetime pathological gambler 5+
Page 14
Health and wellbeing questions
Core non-gambling questions W1 W2 W3 W4
•Health, K10, readiness to change, life events, recreation, smoking CAGE etc
Additional contextual questions for specific waves
•Global Financial Crises (W2)
•Economic Stimulus Package (W2)
•Vic Bushfires (W2)
•Linked Jackpots (W3)
•Major sporting events (W3)
•Additional social capital (W4)
•Trauma and hardship (W1 and W4)
•Loneliness (W4)
Page 15
Some findings
Prevalence (total stock)
Vic prevalence rate from wave one - 0.7%
Incidence (new cases)
12 month incidence rate
0.36% (95% CI 0.21% - 0.57%)
NODS CLiP2
0.12% (CI – 0.03% - 0.25%) - (of 0.36%) new problem gamblers
0.24% (CI 0.13% - 0.41%) - (of 0.36%) previous history of path/problem gambling ‘relapse’
Page 16
Co-morbidities
Co-morbidity:
•a condition (or disorder) existing simultaneously but independently with another condition in a person, or
•a condition (or disorder) in a person that causes, is caused by, or is otherwise related to another condition in the same person.
Valderas et al. (2009)
Shaffer and Korn (2002) believe that the complex relationships between co-morbid disorders include the possibilities that:
– both disorders are independent of each other
– one disorder protects against the other
– one disorder causes the other
– both disorders share the same cause or are components of a more complex set of symptoms
Page 17
Co-morbidities
Co-morbidities (from various cross sectional studies) and problem gamblers:
•Depression: 37-71%
•Anxiety disorders: 41-60%
•Severe psychological distress: 25-30%
•Personality disorders: 61% (US)
•Nicotine dependence: 47-64%
•Alcohol abuse & dependence: 48-72%
•Drug dependence: 38% (US)
•Suicide ideation: 9-27%
•In Victoria more likely to report poor health, lung conditions, obesity and a disability affecting everyday life (wave one).
Kessler et al. (2008) Petry et al. (2005) Productivity Commission (1999) Thomas & Jackson (2008) Dept of Justice (2009)
Page 18
Co-morbidities
conditions
0%
10%
20%
30%
40%
50%
60%
70%
current
smokers
sign of
alcohol abuse
obesity anxiety depression troubles w ith
w ork
increased
arguments
w ith
someone
close
unable to get
help w hen
needed
non-problem gamblers low risk gamblers moderate risk gamblers problem gamblers
Source: Victorian Gambling Study, 2008 (Sample =15,000, w eighted)
Page 19
Co-morbidities
number of co-occurring conditions
0%
10%
20%
30%
40%
50%
60%
70%
0 1 2 3 4 >=5
non-problem gamblers low risk gamblers moderate risk gamblers problem gamblers
Source: Victorian Gambling Study, 2008 (Sample =15,000, w eighted)
Page 20
Co-morbidities
Yeah I’ve been, I’ve had mental problems since I was little, for social phobia and growing up I was anorexic for ten years, so I didn’t go to school, I was hospitalised my whole teenage adolescence and the children’s but yeah, it was just always depression, obsessive compulsive disorder and ahm, borderline personality disorder, so….
female—qualitative study, Victorian Gambling Study,
I had the accident, I feel like, I can’t do a lot and just you know you get a bit depressed, do you know what I mean?
male—qualitative study, Victorian Gambling Study
But that’s what happens. You get depressed, you go and blow your money and then you’re depressed because you’ve blown your money. So work that out.
male—qualitative study, Victorian Gambling Study
Page 21
Psychological distress
Page 22
Alcohol abuse
Page 23
Life event triggers
Page 24
Co-morbidities
Chicken or egg?
Little research that clarifies how the onset of problem gambling relates temporally to the onset of other disorders.
Question 1 • The relationship between onset (new cases) of high risk
(MR/PG) gambling behaviour and co morbidities
Question 2 • The relationship between onset (new cases) of co morbidities
and high risk (MR/PG) gambling behaviour
Page 25
Co-morbidities
The onset of co-morbidities
Question 2
The significant variables were:
• being male (OR=2.0, CI 1.3-3.0, p=0.002)
• age (OR= 1.02, CI 1.00-1.03, p=0.008)
• disability (OR=2.1, CI 1.9-4.0, p=0.028), and
• PGSI problem gambling risk category (OR=4.2, CI 0.9-18.9, p= 0.061).
Page 26
Co-morbidities
Question 1
The onset of high risk gambling
•Scoring as an ‘at-risk lifetime gambler’ (NODS CLiP2) was significantly associated with new onset of high risk gambling behaviour during the study period (OR=6.3, p=0.007, CI 1.7-23.9).
•Any health condition (OR=2.7, p=0.027, CI 1.1-6.7)
•Current smoker (OR=2.7, p=0.035, CI 1.1-6.8)
Further analysis on any health condition…
•Participants with anxiety were x 4 more likely to develop MRPG (OR=4.0 p=0.036, CI 1.1-14.6) [adjusted for NODS and smoking]
Page 27
Transitions
Page 28
PGSI Risk Group Transitions
Wave Four risk groups from Waves One, Two and Three
For example, problem gamblers and their risk group in previous waves
0
10
20
30
40
50
60
70
80
90
100
2008 2009 2010 2011 2008 2009 2010 2011 2008 2009 2010 2011 2008 2009 2010 2011
ZR LRG MRG PG
Zero risk Low Risk Gamblers Moderate Risk Gamblers Problem Gamblers
Page 29
PGSI Risk Group Transitions
PGSI Risk Group Transitions
Wave One risk groups thro Waves Two, Three and Four
For example, problem gamblers and their risk group in subsequent waves
Page 30
Transitions
•MR greatest probability of transitioning to PG - 11%
•Most PG are likely to remain PG, regardless of gender - 71%
•19% of PG likely to decrease to MR, and
•Probability that PG will cease gambling is close to zero - <1%.
Markov chain to predict the probabilities of transitioning in and out of gambling risk states.
Page 31
Lifetime gambling risk compared to
PGSI category across the four waves
Page 32
Frequency of EGM use
W1 to W3
Page 33
Person time
Wave One July 2008 - October 2008 What is person years?
The time at risk for all persons in a population
Each year a participant contributes to a study
= one person year
In our study 3686 participants completed all four waves
= 14,744 person years
Page 34
Person time
Wave One July 2008 - October 2008 Most problem gamblers (71%) were likely to remain problem gamblers from one year to the next Approximately 22% of problem gamblers were likely to decrease to moderate risk The probability that problem gamblers were likely to cease gambling was close to zero (0.1%) Moderate risk (9%) had the greatest probability of becoming problem gamblers Non gamblers or non problem gamblers had a very low probability of becoming problem gamblers (0.1%)
Page 35
Stability probability
CPGSI Category Overall Stability
n % %
NG 2,148 14.57 48.86%
NPG 11,225 76.13 82.51%
LR 896 6.08 35.67%
MR 345 2.34 43.34%
PG 130 0.88 59.09%
Total 14,744 100
Page 36
Challenges and learnings
•Ambitious project
•Multi supplier model (international, national)
•(Baseline + additional) funding
•Time - lack thereof (govt dept)
•Analysis, analysis, analysis (for example, response rate with priorities)
•Definitional changes
•Measurement (PGSI anchors, definitional changes etc)
Page 37
Challenges and learnings
•Attrition (endeavour to counteract)
•Awareness of mortality (loss of research staff)
•Collaboration- international
•‘doh’ moments - what should have been asked
•Familiarity
•Spreading the word
•Replication
Page 38
Where to from here?
Short cohort follow up survey in 2015-2016? (another world first) - report changes over 7-8 years?
Short, sharp three-monthly follow up of participants to see short term transitions?
Examine remaining hypotheses
Secondary analyses currently underway
Use findings for targeted prevention (e.g. MR to PG)
Collaboration with Sweden & New Zealand & USA (article in press) & data pooling
Fact sheets underway
Peer reviewed articles (methods paper submitted)
Delivery of findings via presentations
(Caution: findings need to be confirmed via other studies)
Research team
• Max Abbott
• Rosa Billi
• Sarah Hare
• Damien Jolley
• Penny Marshall
• Paul Marden
• Jan McMillen
• Elmer Villanueva
• Rachel Volberg
• Christine Stone
We would like to acknowledge and
remember Damien Jolley