Commentary on Dowling <i>et al</i>. (2016): Is it time to stop conducting problem gambling prevalence studies?

Markham, Francis; Young, Martin

doi:10.1111/add.13216

Cited by 14 publications

(10 citation statements)

References 12 publications

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“…Future studies using more consistently-collected data could seek to measure the relationship between EGM losses and problem gambling prevalence with more precision. These results support the need to phase out state-based prevalence studies and transition to national problem gambling prevalence studies that are adequately powered to investigate individual jurisdictions and that remain methodologically stable over time [16]. …”

Section: Discussionmentioning

confidence: 57%

“…This is important because problem gambling prevalence estimates are the primary means through which gambling-related harm is monitored by regulators and governments. If prevalence estimates cannot be meaningfully compared, this calls into question validity of the current, routine practice of monitoring problem gambling prevalence using surveys [16]. …”

Section: Introductionmentioning

confidence: 99%

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A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines

et al. 2017

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BackgroundMany 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.MethodsA 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.ResultsMoney 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).ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-017-4413-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines

et al. 2017

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“…The British prevalence data lends itself better than many other datasets to pooling because the different studies had similar approaches to sampling and weighting, recruited similar sample sizes and used the same problem gambling assessments that have a similar latent class structure. The response rates across the surveys are similar (52%, 47%, 56%), and are much higher than some other gambling prevalence surveys ( Markham & Young, 2016 ), where responses have fallen as low as 20%. The British prevalence surveys also appear to concord with many of the best practices identified by Williams and Volberg (2010) .…”

Section: Introductionmentioning

confidence: 50%

“…There is also a close degree of correspondence between the assessments used in UK gambling prevalence research and screens administered by healthcare practitioners to gamblers seeking treatment ( Bowden-Jones & George, 2015 ). Recent commentaries have suggested that rather than comparing disordered gambling prevalence across timeframes or jurisdictions, the greatest benefit from prevalence research has emerged from comparing across sub-samples of gambler ( Markham & Young, 2016 ). This paper pools data from multiple British surveys using similar survey designs to uncover the predictors of latent class membership from socio-demographic correlates and other addictive behaviours, building on latent class analyses (LCAs) of problem gambling assessments that have consistently observed three subtypes of gambler ( McBride et al, 2010 , Carragher and McWilliams, 2011 , James et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Sociodemographic predictors of latent class membership of problematic and disordered gamblers

James

O’Malley

Tunney

2016

Addictive Behaviors Reports

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This paper reports a series of analyses examining the predictors of gambling subtypes identified from a latent class analysis of problem gambling assessment data, pooled from four health and gambling surveys conducted in Britain between 2007 and 2012. Previous analyses have indicated that gambling assessments have a consistent three class structure showing quantitative and potentially qualitative differences. Bringing this data together is useful for studying more severe problem gamblers, where the small number of respondents has been a chronic limitation of gambling prevalence research. Predictors were drawn from sociodemographic indicators and engagement with other legal addictive behaviours, namely smoking and alcohol consumption. The pooled data was entered into a multinomial logistic regression model in which class membership was regressed along a series of demographic variables and survey year, based on previous analyses of gambling prevalence data. The results identified multiple demographic differences (age, general health, SES, being single, membership of ethnic minority groups) between the non-problem and two classes endorsing some problem gambling indicators. Although these two groups tended to share a sociodemographic profile, the odds of being male, British Asian and a smoker increased between the three groups in line with problem gambling severity. Being widowed was also found to be associated with the most severe gambling class. A number of associations were also observed with other addictive behaviours. However these should be taken as indicative as these were limited subsamples of a single dataset. These findings identify specific groups in which gambling problems are more prevalent, and highlight the importance of the interaction between acute and determinant aspects of impulsivity, suggesting that a more complex account of impulsivity should be considered than is currently present in the gambling literature.

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“…The monitoring of problem gambling via general population surveys is hampered by the small proportion of the general population who report gambling problems [10,11]. Monitoring EGM venues-the locations where problem gamblers can be found most easily -presents a promising opportunity for public health surveillance.…”

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confidence: 99%

Commentary on van der Maas et al. (2017): Going where the action is

Markham

Young

2017

Addiction

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Commentary on Dowling et al. (2016): Is it time to stop conducting problem gambling prevalence studies?

Cited by 14 publications

References 12 publications

A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines

A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines

Sociodemographic predictors of latent class membership of problematic and disordered gamblers

Commentary on van der Maas et al. (2017): Going where the action is

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