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      The identification of Australian low-risk gambling limits: A comparison of gambling-related harm measures

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          Abstract

          Background and aims

          Problem gambling severity and gambling-related harm are closely coupled, but conceptually distinct, constructs. The primary aim was to compare low-risk gambling limits when gambling-related harm was defined using the negative consequence items of the Problem Gambling Severity Index (PGSI-Harm) and the Short Gambling Harms Scale items (SGHS-Harm). A secondary aim was compare low-risk limits derived using a definition of harm in which at least two harms across different domains (e.g. financial and relationship) were endorsed with a definition of harm in which at least two harms from any domain were endorsed.

          Methods

          Data were collected from dual-frame computer-assisted telephone interviews of 5,000 respondents in the fourth Social and Economic Impact Study (SEIS) of Gambling in Tasmania. Receiver operating characteristic (ROC) curve analyse were conducted to identify low-risk gambling limits.

          Results

          PGSI-Harm and SGHS-Harm definitions produced similar overall limits: 30–37 times per year; AUD$510–$544 per year; expenditure comprising no more than 10.2–10.3% of gross personal income; 400–454 minutes per year; and 2 types of gambling activities per year. Acceptable limits (AUC ≥0.70) were identified for horse/dog racing, keno, and sports/other betting using the PGSI definition; and electronic gaming machines, keno, and bingo using the SGHS definition. The requirement that gamblers endorse two or more harms across different domains had a relatively negligible effect.

          Discussion and conclusions

          Although replications using alternative measures of harm are required, previous PGSI-based limits appear to be robust thresholds that have considerable potential utility in the prevention of gambling-related harm.

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          Most cited references37

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          Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection.

          The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability for classifying disease status. The Youden Index (J), the maximum potential effectiveness of a biomarker, is a common summary measure of the ROC curve. In biomarker development, levels may be unquantifiable below a limit of detection (LOD) and missing from the overall dataset. Disregarding these observations may negatively bias the ROC curve and thus J. Several correction methods have been suggested for mean estimation and testing; however, little has been written about the ROC curve or its summary measures. We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c *) for a biomarker affected by a LOD. We develop unbiased estimators of J and c * via ML for normally and gamma distributed biomarkers. Alpha level confidence intervals are proposed using delta and bootstrap methods for the ML, semi-parametric, and non-parametric approaches respectively. Simulation studies are conducted over a range of distributional scenarios and sample sizes evaluating estimators' bias, root-mean square error, and coverage probability; the average bias was less than one percent for ML and GLM methods across scenarios and decreases with increased sample size. An example using polychlorinated biphenyl levels to classify women with and without endometriosis illustrates the potential benefits of these methods. We address the limitations and usefulness of each method in order to give researchers guidance in constructing appropriate estimates of biomarkers' true discriminating capabilities. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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            Psychological Science Can Improve Diagnostic Decisions.

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              Understanding gambling related harm: a proposed definition, conceptual framework, and taxonomy of harms

              Background Harm from gambling is known to impact individuals, families, and communities; and these harms are not restricted to people with a gambling disorder. Currently, there is no robust and inclusive internationally agreed upon definition of gambling harm. In addition, the current landscape of gambling policy and research uses inadequate proxy measures of harm, such as problem gambling symptomology, that contribute to a limited understanding of gambling harms. These issues impede efforts to address gambling from a public health perspective. Methods Data regarding harms from gambling was gathered using four separate methodologies, a literature review, focus groups and interviews with professionals involved in the support and treatment of gambling problems, interviews with people who gamble and their affected others, and an analysis of public forum posts for people experiencing problems with gambling and their affected others. The experience of harm related to gambling was examined to generate a conceptual framework. The catalogue of harms experienced were organised as a taxonomy. Results The current paper proposes a definition and conceptual framework of gambling related harm that captures the full breadth of harms that gambling can contribute to; as well as a taxonomy of harms to facilitate the development of more appropriate measures of harm. Conclusions Our aim is to create a dialogue that will lead to a more coherent interpretation of gambling harm across treatment providers, policy makers and researchers.
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                Author and article information

                Contributors
                Journal
                J Behav Addict
                J Behav Addict
                JBA
                Journal of Behavioral Addictions
                Akadémiai Kiadó (Budapest )
                2062-5871
                2063-5303
                31 March 2021
                April 2021
                April 2021
                : 10
                : 1
                : 21-34
                Affiliations
                [1 ]School of Psychology, Deakin University , Geelong, VIC 3220, Australia
                [2 ]Melbourne Graduate School of Education, University of Melbourne , Parkville, VIC 3010, Australia
                [3 ]Centre for Adolescent Health, Murdoch Children’s Research Institute , Melbourne, Australia
                [4 ]Experimental Gambling Research Laboratory, CQUniversity , Bundaberg, QLD, Australia
                [5 ]The Social Research Centre, Australian National University , Melbourne, VIC 3000, Australia
                Author notes
                [* ]Corresponding author. E-mail: nicki.dowling@ 123456deakin.edu.au
                Article
                10.1556/2006.2021.00012
                8969860
                33793416
                f292a116-ada5-422c-9cc5-362d4eb12b84
                © 2021 The Author(s)

                Open Access. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated.

                Page count
                Tables: 6, Equations: 0, References: 38, Pages: 14
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                low-risk limits,low-risk guidelines,gambling,responsible gambling,gambling harm,gambling-related harm

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