1. Canadian Low- Risk Gambling Limits: New Evidence and Limitations Shawn R. Currie, Ph.D. April 10, 2010

2. Gambling in Canada Legal in all provinces 76% of Canadians have gambled in the last year (Cox et al., 2005) Gambling venues and opportunities include: 87,000 electronic gaming machines (VLTs and slots) 87,000 electronic gaming machines (VLTs and slots) 33,000 lottery outlets 33,000 lottery outlets 250 race tracks 250 race tracks 60 permanent casinos 60 permanent casinos 25,000 licenses to run temporary bingos, casinos, raffles 25,000 licenses to run temporary bingos, casinos, raffles Large and growing unregulated gambling market in Internet gambling Internet gambling Private poker halls Private poker halls

3. What is Responsible Drinking? Abstain Responsible drinking is No more than 2 drinks per day No more than 2 drinks per day 1 non-drinking day per week 1 non-drinking day per week Drink slowly, avoid intoxication, wait an hour between drinks Drink slowly, avoid intoxication, wait an hour between drinks Don’t drink if driving, operating equipment, pregnant, others. Don’t drink if driving, operating equipment, pregnant, others. Alcoholabuse Alcoholdependence

4. What is Responsible Gambling? Nogambling Don’t gamble alone, for money for basic needs, with borrowed money, chase loses, when drinking. Responsible gambling? -Frequency? -Amount? -Time spent? Problemgambling Pathologicalgambling

5. Percent monthly income spent on gambling activities and harm Average daily alcohol consumption and risk of all-cause mortality Source: Alberta Gambling Prevalence Study (2002) Source: Babor et al. (2003) Males 45 and over

6. Using National Population Health Data to Develop Responsible Gambling Limits

7. Consequences of Gambling (assessed by Canadian Problem Gambling Index) Betting more than can afford to lose. Gambling caused health problems, including stress/anxiety. Gambling caused financial problems. Borrowed money or sold anything to gamble. Gambling caused interpersonal problems. Others criticized your gambling. Felt guilty about gambling. Felt you might have a gambling problem. Harms = Reporting 2 or more negative consequences.

8. N Males2472184121011342745435 Females367921531970994476272 Total 6151 3994 4071 2336 1221 707 CCHS-1.2 Dollars spent per year

9. CCHS-1.2 Frequency by type of gambling

10. Comparison of CCHS-1.2 with AB, BC, and ON data

11. Performance of optimal cut-points across surveys ABONTBCCCHS-1.2 Frequency AUC.77.67.63.81 Optimal cut-off2 - 3X / month 3 -5X / month 2 - 3X / month Sensitivity/specificity92 / 5761 / 6765 / 57 88.3 / 59 Dollars spent AUC.89.75.74.81 Optimal cut-off$80 / month$400 / year$11 - $50 /month $501-$1000 / year Sensitivity/specificity82 / 8461 / 8273 / 67 78 / 70 Percent gross income AUC.91.80 NA.79 Optimal cut-off3%1%1% Sensitivity/specificity78 / 8976 / 66 74 / 74 AUC = area under ROC curve, Swets (1988) guidelines 0.5 – 0.7 = ‘low accuracy’ 0.7 – 0.9 = ‘moderate accuracy’ > 0.9 = ‘high accuracy’

12. Soliciting Expert Opinion Survey Goals: (1) Obtain opinions from 171 gambling experts (researchers, clinicians, policy makers) on the need for low-risk limits (2) Assess the face validity of CCHS-1.2 derived limits

13. Face Validity of Proposed Limits Source: Currie, S. R., Hodgins, D. C., Wang, J. el-Guebaly, N., & Wynne, H. (2008). In pursuit of empirically derived low-risk gambling limits. International Gambling Studies, 8, 207-227.

14. Limitations of the Method Insufficient data to develop game-specific low- risk limits Reliability of gambler’s self-reported expenditure in phone surveys is questionable Lack of agreement on definition of harm in context of gambling Retrospective accounts of gambling harm and expenditure

15. Analysis of Low-Risk Gambling Limits in the Leisure, Lifestyle, Lifecycle Project Investigators: Nady el-Guebaly, MD David Hodgins, PhD Garry Smith, PhD Rob Williams, PhD Don Schopflocher, PhD Rob Wood, PhD Method Starting in 2006, longitudinal cohort study of over 1800 adolescents and adults living in rural and urban Alberta Individuals in five age cohorts range from 13 to 65 years being followed for five years The sample includes persons randomly selected from the general population and persons considered at-risk for problem gambling based on certain criteria. Data collection every 14 months Data collection every 14 months.

16. Leisure, Lifestyle, Lifecycle Project: Demographics at Time 1 Variable Total Adult Completes (N=1372) N% Age 18-20 23-25 23-25 43-45 43-45 63-65 63-6531534140331323.024.929.422.8 GenderMale Female Female60277043.956.1 LocationCalgary Edmonton Edmonton Grande Prairie Grande Prairie Lethbridge Lethbridge57740517022042.129.512.416.0

17. Leisure, Lifestyle, Lifecycle Project: Description of Longitudinal Sample (N=809) VariableN % (weighted) Age 18-20 23-25 23-25 43-45 43-45 63-65 63-6514618228319826273215 GenderMale Female Female3484615050 Employ PT or FT Unemployed5872227525 Health Smokers Good-excellent physical health Good-excellent physical health Good-Excellent mental health Good-Excellent mental health185638737197891 Inclusion criteria: Adults (>17 years) Participated in Time 1 and 2 Exclusion criteria: No gambling at Time 1 and 2

18. Gambling above the Risk Limits at Time 1 & Time 2 (~18 months) Low risk gambling limit Time 1 Time 2 Chi- square N % (weighted) N > 1% income 13712%23723%140.71* > $500/year 877.5%19318%74.94* > 2-3 times/month 16817%22621%88.65* Any risk factor 23723%36836%136.04* * p <.0001

19. Gambling above the Risk Limits at Time 1 & Time 2

20. Cross-Sectional Data Time 1 Low risk gambling limit Weighted proportion gambling over limit who report harm Weighted proportion gambling under limit who report harm Odds ratio Chi-square(weighted) > 1% income 31%6%7.365.09* > $500/year 43%6%11.794.71* > 2-3 times/month 29%4%8.685.77* * p <.0001

21. Change in Risk Category between Time 1 & Time 2 N = 809 Gamblers n = 400 Low risk at T1 & T2 (59%) n = 175 Low risk (T1) to High risk (T2) (19%) n = 50 High risk (T1) to Low risk (T2) (6%) n = 184 High risk at T1 & T2 (16%) Low risk = gambling below all risk limits High risk = exceeds at least one low risk limit

22. Game preferences at Time 1 & Time 2 (all gamblers)

23. Game preferences at Time 1 & Time 2 (gamblers shifting from low risk to high risk) p <.0001 p <.001 p <.01 p <.0001

24. Game preferences at Time 2 (gamblers who were low risk at Time 1) p <.0001 p = NS p <.05 p <.001

25. Gamblers who shift from low risk to high risk on frequency of gambling Time 1 Low risk < 2-3 times/mo N= 637 (83%) Time 2 High Risk > 2-3 times/mo N= 129 (23%) Low Risk < 2-3 times/mo N= 507 (77%) Harm N = 22 (17%) No harm N = 107 (83%) Harm N = 55 (9%) No harm N = 452 (90%) Χ 2 = 3.21, p =.07

26. Gamblers who shift from low risk to high risk on percent of income Time 1 Low risk < 1% income N= 635 Time 2 High Risk > 1% income N= 136 (37%) Low Risk < 1% income N=499 (63%) Harm N = 31 (23%) No harm N = 105 (77%) Harm N = 42 (8%) No harm N = 457 (92%) Χ 2 = 22.15, p <.0001

27. Gamblers who shift from low risk to high risk on dollars spent Time 1 Low risk < $500/yr N = 722 Time 2 High Risk > $500/yr N= 137 (15%) Low Risk < $500/yr N=585 (85%) Harm N = 31 (22%) No harm N = 106 (78%) Harm N = 59 (9%) No harm N = 526 (91%) Χ 2 = 13.53, p <.001

28. Gamblers who shift from low risk to high risk on any low risk limit Time 1 Low risk on all limits N = 549 Time 2 High Risk on at least one limit N= 175 (25%) Low risk on all limits N=374 (75%) Harm N = 31 (18%) No harm N = 144 (82%) Harm N = 26 (7%) No harm N = 348 (93%) Χ 2 = 12.52, p <.001

29. Impact of Changing Risk Category on DSM Symptoms of Pathological Gambling Change from Time 1 to 2 CIDI-Gambling Time 2 (Mean symptom count) T-valueSignif Harm No harm Differ Low risk to high risk (n = 175) 1.65 (0.43) 0.13 (0.05) 1.51 4.81 4.81 **<.0001 Low risk to low risk (n = 374) 0.19 (0.19) 0.07 (0.03).09 1.31 * NS

30. Gamblers who shift from high risk to low risk on any low risk limit Time 1 High risk on at least one limit N = 234 Time 2 High Risk on at least one limit N= 184 (73%) Low risk on all limits N=50 (28%) Harm N =59 (31%) No harm N = 125 (69%) Harm N = 10 (19%) No harm N = 40 (81%) Χ 2 = 3.41, p = 0.16

31. Conclusions Cross-sectional data: The risk curve method appears to be valid for identifying low risk gambling limits Limitations of current population data dictate that disseminating actual limits to public would be premature at this point Longitudinal data: Large number of people shift to higher risk gambling and begin to experience more harm Change from low-risk to high risk gambling based on exceeding the quantitative limits is associated with: - increased harm - preference for higher risk forms of gambling (EGMs, casino gambling, instant win tickets) - more symptoms of pathological gambling.