Electronic gaming machine expenditure and problem gambling: Investigating the relationships for individuals, gambling venues and jurisdictions

Transcription

1 Electronic gaming machine expenditure and problem gambling: Investigating the relationships for individuals, gambling venues and jurisdictions Francis Markham AGRI 2017, 7 April 2017.

2 Disclosure of Potential Conflict of Interest Presenter Disclosure I have received research funding from The Community Benefit Fund of the Northern Territory, and The ACT Gambling and Racing Commission I am a member of the Public Health Association of Australia

3 Structure of talk today: 1 Individuals 2 Venues 3 Jurisdictions

4 Structure of talk today: 1 Individuals 2 Venues 3 Jurisdictions

5 Loss problem gambling curve shape Conventional wisdom suggests a J-shaped relationship between expenditure and harm for individuals: Reno Model (Blaszczynski, Ladouceur & Shaffer, 2004): the first of six fundamental assumptions contained within the responsible gambling framework is that safe levels of gambling participation are possible. Shaffer: Gambling, like drinking alcohol, displays a dose response association that reflects hormesis as an underlying process. Blaszczynski A, Ladouceur R, Shaffer HJ. A Science-Based Framework for Responsible Gambling: The Reno Model. J Gambl Stud. 2004;20(3): Shaffer HJ. From Disabling to Enabling the Public Interest: Natural Transitions From Gambling Exposure to Adaptation and Self-Regulation. Addiction. 2005;100(9):

6 Loss problem gambling curve shape I searched for secondary data sets which: were nationally representative; were from a country reporting high levels of gambling losses; included a validated screening test for problem gambling; included questions about gambling expenditure in which losses were recorded as a continuous variable; and in which questions about gambling losses were disaggregated by gambling product.

7 Loss problem gambling curve shape Four eligible surveys were found: Australian National Gambling Survey (1999). The SOGS (last 12 months) was administered to 1,240 adults. Canadian National Validation Survey (2000). The PGSI was administered to 3,120 adults. Finnish Gambling Survey 2011 administered the PGSI to 3,451 people aged The Gambling in Norway (2002) survey administered the 12-month NODS to 5,235 Norwegians aged 15-74

8 I analyzed four studies with bootstrapped multiple linear regression, adjusting for demographics:

9 Loss problem gambling curve shape Different results for different gambling products:

10 Loss problem gambling curve shape Different results for different gambling products:

11 Summary of results: Loss problem gambling curve shape Total gambling losses problem gambling curves are all r -shaped EGM losses were the most predictive of problem gambling (R 2 = 0.26, 0.23, 0.20, 0.04) EGM loss problem gambling curves were r - shaped (but quadratic term was not significant in Finland) Only curves that may have been J -shaped were lotteries in Australia and racing in Finland and these were not significant

12 Implications Loss problem gambling curve shape Public health messaging should distinguish between different products EGMs are the most risky No evidence for a J -curve or a safe region where increasing consumption does not increase risk Low-risk consumption guidelines consider absolute risk (e.g. like Australia s drinking guidelines, which are set at a lifetime level of risk of death of 1 in 100)

13 Loss problem gambling curve shape Gambling on EGMs is more like tobacco than alcohol the less you gamble, the lower your risk Traditional messaging oriented around reduce, restrict, limit, ban make sense for determinants that have a linear relationship with health outcomes, as with tobacco and mortality. Chokshi et al. (2015), JAMA, doi: /jama

14 Next steps? Loss problem gambling curve shape Evaluation of the robustness of these findings with different measures of exposure (i.e. time, money as a proportion of income) and harm (harmful outcomes beyond problem gambling) Longitudinal analysis evaluating player loss and harm for individual products. Keen to speak to data custodians of the VGS, LLLP, QLS, Swelogs, NZNGS and MAGIC studies regarding suitability and data sharing.

15 Loss problem gambling curve shape

16 Structure of talk today: 1 Individuals 2 Venues 3 Jurisdictions

17 Loss & PG in venues Rates of problem gambling vary substantially between venues. How does this relate to player loss? 5.5% Skycity Casino 0.4% Darwin Trailer Boat Club

18 Loss & PG in venues For each venue (n = 62), we compiled: 1. Prevalence of gambling-related harm among patrons (2 PGSI items). Source: survey, n = 7, Monthly poker-machine expenditure. Source: NT Department of Justice 3. Population of estimated trade-area and census. Source: Spatial modelling based on survey

19 Loss & PG in venues Binomial GLM, adjusted for heteroskedasticity 9% 18%

20 Loss & PG in venues Implications: 1. Gambling regulators can identify the most harmful venues from a cheap and repeatable desktop analysis. 2. A further analysis of the characteristics that makes some venues more profitable than others might suggest avenues for public health interventions.

21 Loss & PG in venues

22 Structure of talk today: 1 Individuals 2 Venues 3 Jurisdictions

23 Loss & PG in jurisdictions What about states and territories? Meta-regression of EGM losses and problem gambling prevalence: 1. Systematic search for Australian problem gambling prevalence studies. 2. Extract administrative data on gaming machine expenditure for each state and territory at the time of the survey. 3. Randomeffects Bayesian meta-regression analysis, with informative priors to adjust for heterogeneity

24 Loss & PG in jurisdictions What about states and territories? Random effects Bayesian meta-regression analysis, with informative priors to adjust for heterogeneity Adjusting for: 1. Total player loss on EGMs and in casinos in the jurisdiction, as a percentage of HDI 2. Year of survey 3. Methodological variations: 1. Screen (PGSI vs. SOGS) 2. Administration mode (face-to-face vs. telephone) 3. Screen threshold (regular gamblers vs. everyone)

25 1. A total of over 265,000 respondents to 41 eligible studies. Loss & PG in jurisdictions

26 Loss & PG in jurisdictions 1. A total of over 265,000 respondents to 41 eligible studies. 2. Mean problem gambling prevalence of 0.9% of adults. 3. Huge amount of heterogeneity among studies (I 2 = 0.95)

27 Loss & PG in jurisdictions 1. A total of over 265,000 respondents to 41 eligible studies. 2. Mean moderate risk PG prevalence of 1.8% of adults. 3. Similar amount of heterogeneity among studies (I 2 = 0.94)

28 Loss & PG in jurisdictions Each increase of 1% of household disposable income spent on gaming machines was associated with problem gambling prevalence estimates that were 1.3 times greater. R 2 = 0.66, I 2 = 0.87

29 Loss & PG in jurisdictions But for moderate risk (PGSI 3-7, SOGS 3-4)? No significant results for any coefficient, including player loss. R 2 = 0.00

30 Loss & PG in jurisdictions Implications: 1. Problem gambling prevalence was associated with EGM and casino gambling losses. Prevalence ratio = 1.33 times (95% Cr.I ). Adds tentative support for Total Consumption Theory

31 Loss & PG in jurisdictions Implications: 2. A great deal of heterogeneity remained in problem gambling prevalence estimates (after adjusting for 3 methodological differences, year of study and EGM and casino losses) I 2 = 0.87 (i.e. 87% of residual variation is non-sampling error) The validity of comparing problem gambling prevalence estimates is questionable even after adjusting for methodological differences.

32 Loss & PG in jurisdictions Implications: 3. No model explained any meaningful amount of the variation in moderate-risk problem gambling prevalence R 2 = 0.00 It is uncorrelated with player loss, survey year, PG screen, PG screen frequency threshold or survey mode. What does MRPG (i.e. PGSI 3-7) measure? Why is it unrelated to things we think it should be related to?

33 Loss & PG in jurisdictions

34 Questions? Contact: Francis Markham