The Evolution of Gambling-Related Harm Measurement: Lessons from the Last Decade

Harm-to-self from gambling: A national study of Australian adults

Aims

Understanding how gambling harm is distributed is essential to inform effective harm reduction measures. This first national Australian study of gambling harm-to-self examined the extent, distribution, risk factors, and health related quality of life (HRQoL) impacts of this harm.

Methods

A Random Digit Dialling sample of 15,000 Australian adults was weighted to key population variables. Key measures included the Gambling Harms Scale-10 (GHS-10), PGSI, SF-6D, gambling behaviours, and demographics. Analyses included ordinal logistic regression.

Results

Amongst gamblers, 14.7% reported harm on the GHS-10, including 1.9% reporting high-level harm. While high-level harm occurred mainly in the problem gambling group (77.3%), other PGSI groups accounted for most of the more prevalent low (98.5%) and moderate (87.2%) harms reported. Proximal predictors of greater harm were use of online gambling and more frequent gambling on electronic gaming machines (EGMs), race betting sports betting, poker, skin gambling, scratchies, and loot box purchasing. Distal predictors were being younger, male, single, Aboriginal or Torres Strait Islander, and speaking a non-English language at home. At the population level, the greatest aggregate HRQoL impacts were amongst lower-risk gamblers, confirming the results of other studies regarding the 'prevention paradox'.

Conclusions

The distribution of harm across gambler risk groups indicates the need for preventive measures, not just interventions for problem gambling. Reducing harm requires modifying product features that amplify their risk, especially for EGMs, race betting and sports betting that are both inherently risky and widely used. Gambling harm exacerbates health disparities for disadvantaged and vulnerable groups, requiring targeted resources and support.

Changes in some health and lifestyle behaviours are significantly associated with changes in gambling behaviours: Findings from a longitudinal New Zealand population study

Although a large number of studies have investigated associations between risky gambling behaviours and health, lifestyle and social factors, research has not focused on changes in these factors and associations with changes in gambling risk level. This study utilised existing data from the four waves of the longitudinal New Zealand National Gambling Study to examine associations between changes in substance use, mental and physical health, and quality of life and deprivation with changes in gambling risk level over time. A Markov chain transition model was used to perform these analyses using data from participants who had completed all four waves (11,080 data transitions). Although changes in various covariates were associated with changes in all gambling risk levels, the highest number of significant factors was for transitioning into risky gambling from non-problematic gambling, including development, or continuation, of several negative health and lifestyle factors that may possibly be alleviated by transitioning out of risky gambling. These findings highlight the importance of screening for gambling behaviours when assisting people with substance use, health issues, or social situations or conditions in order to provide appropriate and effective social, health and treatment supports for people whose gambling behaviour increases over time.

Validation of the 7-Item Domain-General Gambling Harm Scale (DGHS-7)

Introduction

Gambling can cause negative consequences affecting finances, work/study, physical and mental health, relationships, law abidingness, and the community. Although existing measures enable investigations of gambling harms, there is still a need for a brief measure covering the full range of gambling related harms.

Methods

We validated a 7-item domain-general harm scale (DGHS-7) using data from a cross-sectional survey of United Kingdom residents reporting gambling within the last 12 months (n = 2558, 62.4% women, mean age 40.1 years (SD = 12.5)). The DGHS-7 was investigated in terms of factor structure, measurement invariance, and convergent validity with a comprehensive 72-item checklist of gambling harm, the Short Gambling Harms Screen (SGHS), and the Problem Gambling Severity Index (PGSI). Discriminative validity was checked against the Personal Wellbeing Index (PWI). Internal consistency was also calculated.

Results

Confirmatory factor analysis supported a one-factor solution (χ² = 136.991, df = 14, χ²/df = 9.785, p <.001, CFI = 0.999, RMSEA = 0.059, 90% CI [0.050, 0.068]). Measurement invariance was supported for gender and binary categorization of age and income (ΔCFI = 0.001). The DGHS-7 correlated strongly with the 72-item checklist (r_s = 0.824), the SGHS (r_s = 0.793), the PGSI (r_s = 0.768), and moderately with the PWI (r_s = -0.303). Cronbach's alpha = 0.91 and ordinal alpha = 0.96 indicated good internal consistency.

Conclusions

Psychometric support was found for a brief measure covering all recognized domains of gambling harm. The DGHS-7 is useful for researchers needing a generic and short measure for epidemiological and other studies calling for short scales.

How gambling problems relate to health and wellbeing in Australian households: Evidence from the Household Income and Labour Dynamics of Australia Survey

Gambling harms can impact the health and wellbeing of both individuals who gamble and those close to them. While harms occur across a spectrum of gambling risk levels, most research is conducted on people close to those gamblers who have severe problems. This study examined the health and wellbeing of people living with gamblers across the entire spectrum of gambling risk levels, via secondary analysis of the nationally-representative Household Income and Labour Dynamics in Australia Survey (HILDA). The subsample of interest comprised 13,698 respondents without a gambling problem of their own, aged between 15 and 84, and who lived in households where all residents completed the Problem Gambling Severity Index (PGSI). Health and wellbeing were measured via the SF-6D, the SF-36, and subjective wellbeing measures. Compared to those living in non-problem gambling households, participants living in problem-gambling households reported significantly lower mean SF-6D scores, lower scores on the SF-36 mental health domain, and significantly less satisfaction with both their financial situation and with feeling part of their local community. Participants living in moderate-risk gambling households also reported less satisfaction with their financial situation than those in non-problem gambling households. Conclusions: The results indicate that measurable impacts to the health and wellbeing of those living with gamblers occur predominantly at the more severe end of the risk level spectrum, except for financial dissatisfaction, which is also evident in those residing with gamblers categorised as moderate-risk.

Separating problem gambling behaviors and negative consequences: Examining the factor structure of the PGSI

There is some uncertainty on how to best conceptualise and measure problem gambling and debate as to whether it is helpful to differentiate the behavioral features of problematic gambling from the negative consequences of gambling. The current study explores this issue by examining the factor structure of a commonly-used problem gambling measure, the Problem Gambling Severity Index (PGSI), as administered to respondents in the 2018 Northern Territory Gambling Prevalence and Wellbeing Survey (n = 3,740 gamblers). Confirmatory factor analyses revealed a two-factor solution offered significant improvement in fit over the one-factor model. Further, the two factors explained unique variance in the number of gambling-related harms experienced by respondents. Although the two factors were highly correlated, the current findings indicate problem gambling behaviors are related to the negative consequences of gambling, but these are not necessarily synonymous. This suggests isolating behavioral and consequential elements of gambling may have utility in public health interventions for gambling that, while concerning, falls below a clinically-significant threshold. Similarly, clinically-oriented research may benefit by measuring the behavioral features, as these components are important targets for individual-level interventions.

Validating the short gambling harm screen against external benchmarks

BACKGROUND AND AIMS

The Short Gambling Harm Screen (SGHS) is currently the most frequently applied dedicated measure of gambling-related harm (GRH), though concerns relating to scale validity have been expressed. The current study aimed to address criticisms that several SGHS items do not depict genuine harms that may occur as a result of gambling, causing the scale to overestimate harm. Specifically, we aimed to test convergence between the SGHS and its constituent items with: (1) wellbeing, and (2) psychological distress.

METHODS

To test criterion validity of both the scale and the items, retrospective analyses of survey data from 2,704 Australian adults (36% non-gamblers; 64% gamblers) were conducted. Subjective wellbeing and psychological distress scores, captured using the Personal Wellbeing Index (PWI) and the Kessler-6 Psychological Distress Scale (K6), respectively, were used as external (non-gambling) benchmarks. A total of 428 (16%) respondents scored at least 1 on the SGHS.

RESULTS

Monotonic decreases and increases, corresponding to poorer personal wellbeing and higher psychological distress, were found with each additional SGHS score increase. Gamblers endorsing a single SGHS item reported lower wellbeing and higher psychological distress than both non-gamblers and gamblers who scored zero on the SGHS.

DISCUSSION AND CONCLUSION

These results show that the SGHS is a valid measure of GRH and contradict suggestions that low scores on the SGHS do not indicate true harm. The SGHS represents a valid and innovative short screening tool to measure GRH in population prevalence studies.

Legacy Gambling Harms: What Happens Once the Gambling Stops?

Purpose of Review

Legacy gambling harm refers to adverse consequences that extend past the period where people are actively gambling at harmful levels. These harms can affect the gambler, people close to them and the wider community. This article reviews current research that investigates legacy harms; the types of legacy harm, how long they last and whether evidence suggests these harms are real or instead imagined injuries or reflections on past regrets.

Recent Findings

Legacy harms to individuals can be broadly categorised as including financial, relationship, emotional/psychological, health, culture, work/study and criminal/deviance harms. In addition, legacy harms affect entire communities by drawing funds and social capital away from vulnerable communities, leaving them socially, culturally and materially impoverished. Most legacy harms that accrue to gamblers have a half-life of 4 years, although financial harms last somewhat longer at 5 years. Greater distance in time from a past gambling issue is reliably and positively related to health and well-being indicators, including the Health Utility Index and the Australian Unity Wellbeing Index, which suggests that legacy gambling harms are real and have a lasting impact on well-being, rather than just imagined hardship from prior gambling difficulties.

Summary

These findings suggest programme and policy development to support gamblers in reducing and managing their legacy harms, rather than focusing only on relapse prevention.

Harm as reported by affected others: A population-based cross-sectional Finnish Gambling 2019 study

This study investigates the prevalence of being an affected other (AO) of a person with problem gambling; and the associations between being an AO and socio-demographics, gambling behaviour, health-related correlates and the amount and type of gambling-related harm (GRH) for the AOs. Furthermore, perspectives of the affected family members (AFMs) and close friends (ACFs) were acknowledged. Cross-sectional, random sample Finnish Gambling population-based survey data (n = 3,994) were collected in 2019. AOs were identified using a question with seven options. Information on GRH was sought using structured questions. One-fifth (21.2 %) of all respondents were AOs, men being typically ACFs and women being more often AFMs. Being an AO was associated with younger age, gambling participation, having a gambling problem of their own and health barriers such as psychological distress. AFMs experienced GRH more often and the amount of different GRHs was greater among the AFMs. The most common harm category experienced by the AOs was emotional harm. Both health-related issues and the amount of GRHs was largest among the AFMs. A substantial amount of GRH was also experienced by ACFs. The study suggests that support could be tailored for AFMs and ACFs, based on their AO status and individual needs. A public health approach for effective harm prevention in primary, secondary and tertiary levels are discussed.

Benchmarking gambling screens to health-state utility: the PGSI and the SGHS estimate similar levels of population gambling-harm

BACKGROUND

Both the Problem Gambling Severity Index (PGSI) and the Short Gambling Harms Screen (SGHS) purport to identify individuals harmed by gambling. However, there is dispute as to how much individuals are harmed, conditional on their scores from these instruments. We used an experienced utility framework to estimate the magnitude of implied impacts on health and wellbeing.

METHODS

We measured health utility using the Short Form Six-Dimension (SF-6D), and used this as a benchmark. All 2603 cases were propensity score weighted, to balance the affected group (i.e., SGHS 1+ or PGSI 1+ vs 0) with a reference group of gamblers with respect to risk factors for gambling harm. Weighted regression models estimated decrements to health utility scores attributable to gambling, whilst controlling for key comorbidities.

RESULTS

We found significant attributable decrements to health utility for all non-zero SGHS scores, as well as moderate-risk and problem gamblers, but not for PGSI low-risk gamblers. Applying these coefficients to population data, we find a similar total burden for both instruments, although the SGHS more specifically identified the subpopulation of harmed individuals. For both screens, outcomes on the SF-6D implies that about two-thirds of the 'burden of harm' is attributable to gamblers outside of the most severe categories.

CONCLUSIONS

Gambling screens have hitherto provided nominal category membership, it has been unclear whether moderate or 'at-risk' scores imply meaningful impact, and accordingly, population surveys have typically focused on problem gambling prevalence. These results quantify the health utility decrement for each category, allowing for tracking of the aggregate population impact based on all affected gamblers.

18-Item Version of the Short Gambling Harm Screen (SGHS-18): Validation of Screen for Assessing Gambling-Related Harm among Finnish Population

Background and aims: It is common for gambling research to focus on problem and disordered gambling. Less is known about the prevalence of gambling-related harms among people in the general population. This study aimed to develop and validate the 18-item version of the Short Gambling Harms Screen (SGHS-18). Methods: Population-representative web-based and postal surveys were conducted in the three geographical areas of Finland (n = 7186, aged 18 or older). Reliability and internal structure of SGHS-18 was assessed using coefficient omega and via confirmatory factor analysis (CFA). Four measurement models of SGHS-18 were compared: one-factor, six-factor, a second-ordered factor model and a bifactor model (M4). Results: The analysis revealed that only the bifactor model had adequate fit for SGHS-18 (CFI = 0.953, TLI = 0.930, GFI = 0.974, RMSEA = 0.047, SRMR = 0.027). The general factor explained most of the common variance compared to specific factors. Coefficient omega hierarchical value for global gambling harm factor (0.80) was high, which suggested that SGHS-18 assessed the combination of general harm constructs sufficiently. The correlation with the Problem and Pathological Gambling Measures (PPGM) was 0.44, potentially reflecting that gambling harms are closely—although not perfectly—aligned with the mental health issue of problem gambling. SGHS-18 scores were substantially higher for participants who gambled more often, who spent more money or who had gambling problems, demonstrating convergent validity for the screen. Discussion: The SGHS-18 comprehensively measures the domains of gambling harm, while demonstrating desirable properties of internal consistency, and criterion and convergent validity.

Gambling-Related Harms for Affected Others: A Finnish Population-Based Survey

Aims This study explores the prevalence of being a past-year affected other (AO) of a problem gambler by gender. The aims were to study the amount and type of gambling-related harms (GRHs) for subgroups of AOs and to distinguish GRH profiles for AO subgroups. Methods A total of 7186 adults aged 18 years and over participated in the Gambling Harms Survey evaluating year 2016. The data were analyzed using descriptive statistics and binary logistic regression. Results Of all respondents, 12.9% were defined as past-year AOs (women 13.7%; men 12.1%). The proportion of affected non-family members (ANFs) was 8.4%, and 5.6% were affected family members (AFMs). AFMs were usually women, and ANFs were usually men. Emotional, relationship, and financial harms were the most common types of harm. The odds of experiencing financial harm were highest for the 18- to 34-year-olds (OR 1.82) and for those whose partner/ex-partner had a gambling problem (OR 3.91). Having a parent/step-parent (OR 1.93) and child/stepchild (OR 3.64) increased the odds of experiencing emotional harm, whereas male gender (OR 0.50) and being an ANF (OR 0.58) decreased emotional harm. Relationship harm was evident for partners/ex-partners (OR 1.97-5.07). Conclusions GRH profiles for AO subgroups varied, which emphasizes the need for effective harm minimization strategies for those in need.