Quantifying Problem Gambling: Explorations in measurement. Nigel E. Turner, Ph.D. Centre for Addiction and Mental Health

Transcription

1 Quantifying Problem Gambling: Explorations in measurement Nigel E. Turner, Ph.D. Centre for Addiction and Mental Health

2 Original abstract Abstract: Over the past few years I had conducted several studies s in various contexts including clients in treatment, school students, university students, the general public (including telephone, mail, and in person interviews), experimental studies of gambling, computer simulations, and offenders in prison, using multiple method of assessment and multiple correlates of problem gambling. In this paper I will examined the relationship between these various methods and correlates of problem gambling and make recommendations regarding the future of problem gambling assessment. The focus will be on the commonalities across the studies in order to draw a complete picture of the problem gambler. Although the study will employ some modelling, the focus will be on the art of modelling, rather than on the mathematics. Too vague, too much to include in one talk.

3 Publications Based on Turner, N.E., Preston, D.L., Saunders, C., McAvoy,, S., & Jain, U. (in press). The relationship of problem gambling to criminal behaviour in a sample of Canadian male federal offenders. Journal of Gambling Studies.. (available on line) And Turner, N.E., Jain, U., Spence, W., & Zangeneh,, M, (2008). Pathways to pathological gambling: Component analysis of variables related to pathological gambling. International Gambling Studies, 8(3),

4 Acknowledgements Team: Dr. Denise Preston, CSC Steven McAvoy, CAMH Crystal Saunders, CAMH Dr. Umesh Jain, CAMH Funded by the Ontario Problem Gambling Research Centre & MHLTC

5 Gambling can be exciting Can be dangerous Can lead to ruin

6 Focus on one type of ruin 254 male offenders in Millhaven Assessment Unit of Correctional Services of Canada 86 % first time federal offenders; only 14% were first time offenders Federal means their sentence is at least 2 years long Recruited by 2 research assistants from CAMH No incentives other than being out of cell for a couple of hours

7 Why this population High rate of PG in offender populations Both criminal behaviour and PG involve poor impulse control Prison is one of the more serious consequences of PG Prison -- a bad alternative to treatment Limitation: all male, have right to not participate, response rate 37%

8 Methods Multiple questionnaires: SOGS, DSM-IV, CPGI. Questionnaires done in groups. Interviews and file review used to check gambling and criminal history.

9 Basic question: How well do various measures, Thresholds. measure PG? Factor loadings (first factor). Convergence. Correlates.

10 Correlates (based on Turner, et al., 2008) Harmful consequences (12 items) Mood: Depression, Social anxiety, Zung Anxiety Cognition: REKT Experiential: Large win ever, won first time, won early days, Impulse: age first gambled, Impulsivity, Current ADHD, ADHD < 12

11 SOGS The first accurate measure of PG Lesieur & Blume (1987). 20 items scored as yes/no 5 yes answers = probable PG Supported by numerous studies

12 DSM-IV IV-TR American Psychiatric Association. (2000). 10 items scored as yes/no 5 yes answers = PG Requires a clinical interview The gold standard We use it as a self report scale

13 CPGI Ferris & Wynne (2001). 9 items scored on 4 point scale (0 to 3) Score of 8 = Severe problem gambling Score of 3 to 7 = Moderate risk First scale to formally provide sub-clinical (moderate) standard and try to validate it.

14 General Population Prevalence Pathological (PG) = 1.1% Sub-clinical problem (SPG) = 2.8% Offender sample SOGS PG = 13.1%; SPG = 5.4%. SOGS life PG = 15.1%; SPG = 4.7% DSM-IV PG = 6.3%; SPG =9.8%. CPGI PG = 9.4%; SPG = 15.7%. PG: Min 5.2%; Max 14.1%

15 DSM-IV IV-TR response rates Pre-occupied 12.9% Increasing amounts 12.9% Efforts to control 11.9% Restless or irritable 7.5% Escape from problems 11.8% Chase losses 23.1% Lie / conceal 9.4% Committed illegal acts 7.5% Jeopardized relationship /job /educational 5.5% Rely on others / desperate 3.9%

16 DSM-IV IV-TR based items (factor loadings) Pre-occupied 0.69 Increasing amounts 0.58 Efforts to control 0.52 Restless or irritable 0.74 Escape from problems 0.60 Chase losses 0.62 Lie / conceal 0.75 Committed illegal acts 0.62 Jeopardized relationship /job /educational 0.78 Rely on others / desperate 0.72

17 SOGS response rates 3 Go back another day 9.8% 4 Claimed to be winning money/lost 13.0% 5 Felt problem 11.2% 6 More than intended 29.8% 7 Criticized 10.7% 8 Felt guilty 13.2% 9 Like to stop but could not 7.9% 10 Hidden betting slips 7.5% 12 Money arguments 8.5% 14 Lost time from work or school 4.0%

18 SOGS response rates 13 Borrowed /not paid them back 6.3% 15a 15b 15c 15d 15e 15f 15g 15h 15i Borrowed / household 7.6% Borrowed / spouse 8.4% Borrowed / other relatives 8.0% Borrowed / banks/loan 2.8% Borrowed / credit cards 5.6% Borrowed / loan sharks 2.8% Borrowed / cashed in stocks/bonds 2.4% Borrowed / sold family property 4.4% Borrowed / borrowed by cheque 2.8%

19 SOGS loadings 3 Go back another day Claimed to be winning money/lost Felt problem More than intended Criticized Felt guilty Like to stop but could not Hidden betting slips Money arguments Lost time from work or school 0.60

20 SOGS loadings 13 Borrowed /not paid them back a 15b 15c 15d 15e 15f 15g 15h 15i Borrowed / household 0.62 Borrowed / spouse 0.54 Borrowed / other relatives 0.65 Borrowed / banks/loan 0.30 Borrowed / credit cards 0.42 Borrowed / loan sharks 0.55 Borrowed / cashed in stocks/bonds 0.29 Borrowed / sold family property 0.48 Borrowed / borrowed by cheque 0.39

21 CPGI (PGSI) 1. Have you bet more than you could really afford to lose? 2. Still thinking about the last 12 months, have you needed to gamble with larger amounts of money to get the same feeling 3. When you gambled, did you go back another day to try to win back the money you lost? 4. Have you borrowed money or sold anything to get money to gamble? 5. Have you felt that you might have a problem with gambling? 6. Has gambling caused you any health problems including stress and anxiety? 7. Have people criticized your betting or told you that you had a gambling problem, regardless or whether or not you though 8. Has your gambling caused any financial problems for you or your household? 9. Have you felt guilty about the way you gamble or what happens when you gamble?

22 CPGI response rates Item Mean 1. Bet more 28.4% 5.5% 2.4% Larger amounts 15.4% 4.7% 1.2% Win back 33.9% 7.5% 1.6% Borrowed 16.9% 2.0% 0.4% Problem 16.5% 4.7% 1.6% Health 14.2% 2.8% 0.4% Criticized 15.0% 3.5% 1.6% Financial 13.8% 3.5% 1.6% Guilty 16.5% 5.1% 2.8% 0.244

23 Factor Structure Item Loading 1. Bet more Larger amounts Win back Borrowed Problem Health Criticized Financial Guilty 0.86

24 Inter-correlations CPGI SOGS- past year SOGS- lifetime DSM-IV IV- TR CPGI 1.00 SOGS -past year SOGS -lifetime DSM-IV IV-TR

25 Harmful consequences CPGI 0.77 SOGS-past year 0.74 SOGS-lifetime 0.78 DSM-IV IV-TR 0.69

26 Mood correlates Depression Social anxiety Zung Anxiety CPGI SOGS-past year SOGS-lifetime DSM-IV IV-TR

27 Cognitive / experiential REKT Large win First time Early days CPGI SOGS-past year SOGS-lifetime DSM-IV IV-TR

28 Impulse correlates First gambled Impulse Current ADHD ADHD < 12 CPGI SOGS-past year SOGS-lifetime DSM-IV IV-TR

29 Interview data 65% of severe problem gamblers reported that gambling led to crime Only 20% of moderate problem gamblers reported that gambling led to crime 37% of moderate vs 8.7% of severe problem gamblers reported that gambling was part of lifestyle

30 How well do various measures, measure PG? Superbly: All three measures are accurate, but vary in terms of thresholds. Prevalence: SOGS > CPGI(PGSI) > DSM-IV IV-TR Is SOGS over inclusion? is DSM-IV IV-TR under inclusion? Some calibration work is needed to assess the thresholds

31 Comparisons with other samples Similar patterns of correlations between PG in prison and in non-correctional populations Magnitude of effects was weaker in terms of mood and REKT in offender sample No clear pattern of one scale being better than the others

32 Psychometrics Low response rates, skewness,, and discrete variables lead to attenuated correlations and artefactual factors Correction for ordinal measures requires large samples sizes (asymptotic) The CPGI has better psychometrics because of scaled items

33 Conclusions CPGI better overall coverage of thresholds Both higher and lower threshold items. The CPGI appears to fall mid way between the SOGS and DSM-IV IV-TR The CPGI has more subclinical scores Threshold differences need to be considered in comparing data sets