The Intersectionality of Gambling Addiction Recovery and Mental Illness: A Machine Learning Approach

Background and Purpose: Given that a variety of gaming activities (i.e., casinos, TV and instant scratch lotteries, sports bettings) are growing, COVID-19 related stress is presenting a severe threat to worsen addictive behaviors (Håkansson’s et al., 2020). Since certain forms of gambling, like internet-based and other forms of gambling activities, could remain unchangeably available to these adults in the COVID-19 related confinement, special attention to the gambling addiction as consequences of the COVID-19 pandemic is needed. In this regard, the purpose of this study is to examine and identify intersectionality for gambling addiction recovery with mental health needs in a behavioral health system.

Methods: The sample of adults aged 18 and above who participated in Midwestern state-funded mental health and addiction services in 2019 and 2020 was selected. Their initial and most recent assessments among adults with the need for gambling addition treatment at the initial assessment were analyzed for the study (N=654). All participants were taken the Adult Needs and Strengths Assessment (ANSA; Lyons, 2009) as the last assessment in either 2019 or 2020, including six domains: (1) strengths, (2) life functioning, (3) cultural factors, (4) caregiver needs, and resources, (5) behavioral health needs, and (6) risk behaviors. This study focused on the ANSA 57 items and four demographic information (i.e., age, gender, race/ethnicity, calendar year). Each ANSA item was rated on a four-point scale, ranging from 0 (non-actionable) to 3 (immediate action required). These ratings were changed into non-actionable (0) and actionable (1) and were examined by a machine learning decision tree model, chi-square automatic interaction detection (CHAID).

Results: Upon repeated decision tree constructions, the intersectionality of the gambling recovery with sexuality, criminal behavior, adjustment to trauma, resiliency, eating disturbance, employment, sleep, legal, residential stability, and substance use was found. The most significant predictor for gambling addiction recovery was substance use. Specifically, it means that adults were more likely to present the gambling addiction recovery when they stayed clean from substance use and did not struggle with impulse control than their peers. In contrast, the current difficulties of substance use, impulse control, decision making, and residential stability were the major barriers to the gambling addiction recovery. The overall accuracy was .8, which indicated that the model was at good distinguishing between gambling addiction recovery and sustaining gambling addiction.

Conclusion and Implications: The findings suggest that staying clean from substance use and impulse control were primary predictors that led to gambling addiction recovery, regardless of the COVID-19 pandemic. The machine learning-based gambling addiction recovery model could be a promising approach to detect the intersection of race/ethnicity and behavioral health challenges and their recovery. It could eventually be a basis for developing a gambling addiction recovery model for adults with needs for gambling addiction treatment at the initial assessment. Further research is also needed to explore the relationship between the identified intersection and other mental health illnesses. Such a relationship study will support the development of an efficient mental health and gambling recovery model.