Background: Electronic gambling machines (EGMs) are in casinos and community venues (hotels and clubs) in all jurisdictions in Australia, except Western Australia (only in casino). EGMs have a range of features that can affect how people gamble, which can influence losses incurred by users. The Northern Territory Government recently changed two EGM policies – the introduction of note acceptors on EGMs in community venues, and an increase in the cap from 10 to 20 EGMs in hotels and 45 to 55 in clubs. This study evaluates two changes in EGM policy on user losses in community venues, and tracks changes in user losses per adult, EGM gambler, and EGM problem/moderate risk gambler between 2005 and 2015. Access online article
Reference: Stevens, M. & Livingstone, C. (2019). Evaluating changes in electronic gambling machine policy on user losses in an Australian jurisdiction. BMC Public Health, 19(517). Retrieved from https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-6814-1
Background: Problem gambling (PG) is a serious public health concern that disproportionately affects people experiencing poverty, homelessness, and multimorbidity including mental health and substance use concerns. Little research has focused on self-help and self-management in gambling recovery, despite evidence that a substantial number of people do not seek formal treatment. This study explored the literature on PG self-management strategies. Self-management was defined as the capacity to manage symptoms, the intervention, health consequences and altered lifestyle that accompanies a chronic health concern.
Methods: We searched 10 databases to identity interdisciplinary articles from the social sciences, allied health professions, nursing and psychology, between 2000 and June 28, 2017. We reviewed records for eligibility and extracted data from relevant articles. Studies were included in the review if they examined PG self-management strategies used by adults (18+) in at least a subset of the sample, and in which PG was confirmed using a validated diagnostic or screening tool.
Results: We conducted a scoping review of studies from 2000 to 2017, identifying 31 articles that met the criteria for full text review from a search strategy that yielded 2662 potential articles. The majority of studies examined self-exclusion (39%), followed by use of workbooks (35%), and money or time limiting strategies (17%). The remaining 8% focused on cognitive, behavioural and coping strategies, stress management, and mindfulness.
Conclusions: Given that a minority of people with gambling concerns seek treatment, that stigma is an enormous barrier to care, and that PG services are scarce and most do not address multimorbidity, it is important to examine the personal self-management of gambling as an alternative to formalized treatment. Article available online
Reference: Matheson, F.L. et al. (2019). The use of self-management strategies for problem gambling: A scoping review. BMC Public Health, 19:445 https://doi.org/10.1186/s12889-019-6755-8
Anne H. Salonen, Jukka Kontto, Riku Perhoniemi, Hannu Alho and Sari Castrén. (2018). BMC Public Health 18(697). doi.org/10.1186/s12889-018-5613-4
Excessive expenditure and financial harms are core features of problem gambling. There are various forms of gambling and their nature varies. The aim was to measure gambling expenditure by game type while controlling for demographics and other gambling participation factors. A further aim was to find out how each game type was associated with gambling expenditure when the number of game types played is adjusted for.
It seems that overall gambling frequency is the strongest indicator of high gambling expenditure. Our results showed that different game types had different effect sizes on gambling expenditure. Weekly gambling on horse races and non-monopoly games had the greatest increasing effect on expenditure. However, different game types also varied based on their popularity. The extent of potential harms caused by high expenditure therefore also varies on the population level. Based on our results, future prevention and harm minimization efforts should be tailored to different game types for greater effectiveness. Full article
By Francis Markham, Martin Young, Bruce Doran, and Mark Sugden.
Background: Many jurisdictions regularly conduct surveys to estimate the prevalence of problem gambling in their adult populations. However, the comparison of such estimates is problematic due to methodological variations between studies. Total consumption theory suggests that an association between mean electronic gaming machine (EGM) and casino gambling losses and problem gambling prevalence estimates may exist. If this is the case, then changes in EGM losses may be used as a proxy indicator for changes in problem gambling prevalence. To test for this association this study examines the relationship between aggregated losses on electronic gaming machines (EGMs) and problem gambling prevalence estimates for Australian states and territories between 1994 and 2016.
Methods: A Bayesian meta-regression analysis of 41 cross-sectional problem gambling prevalence estimates was undertaken using EGM gambling losses, year of survey and methodological variations as predictor variables. General population studies of adults in Australian states and territory published before 1 July 2016 were considered in scope. 41 studies were identified, with a total of 267,367 participants. Problem gambling prevalence, moderate-risk problem gambling prevalence, problem gambling screen, administration mode and frequency threshold were extracted from surveys. Administrative data on EGM and casino gambling loss data were extracted from government reports and expressed as the proportion of household disposable income lost.
Results: Money lost on EGMs is correlated with problem gambling prevalence. An increase of 1% of household disposable income lost on EGMs and in casinos was associated with problem gambling prevalence estimates that were 1.33 times higher [95% credible interval 1.04, 1.71]. There was no clear association between EGM losses and moderate-risk problem gambling prevalence estimates. Moderate-risk problem gambling prevalence estimates were not explained by the models (I 2 ≥ 0.97; R 2 ≤ 0.01).
Conclusions: The present study adds to the weight of evidence that EGM losses are associated with the prevalence of problem gambling. No patterns were evident among moderate-risk problem gambling prevalence estimates, suggesting that this measure is either subject to pronounced measurement error or lacks construct validity. The high degree of residual heterogeneity raises questions about the validity of comparing problem gambling prevalence estimates, even after adjusting for methodological variations between studies.