Income Inequality and the Odds of Online Gambling Among a Large Sample of Adolescents in Canada.
Roman PabayoPriya PatelKaren A PatteScott T LeatherdalePublished in: Journal of gambling studies (2023)
Consistent evidence points to the detrimental effects of income inequality on population health. Income inequality may be associated with online gambling, which is of concern since gambling is a risk factor for adverse mental health conditions, such as depression and suicide ideation. Thus, the overall objective of this study is to study the role of income inequality on the odds of participating in online gambling. Data from 74,501 students attending 136 schools participating in the 2018/2019 Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behaviour (COMPASS) survey were used. The Gini coefficient was calculated based on school census divisions (CD) using the Canada 2016 Census linked with student data. We used multilevel modeling to explore the association between income inequality and self-reported participation in online gambling in the last 30 days, while controlling for individual- and area-level characteristics. We examined whether mental health (depressive and anxiety symptoms, psychosocial wellbeing), school connectedness, and access to mental health programs mediate this relationship. Adjusted analysis indicated that a standardized deviation (SD) unit increase in Gini coefficient (OR = 1.17, 95% CI 1.05, 1.30) was associated with increased odds of participating in online gambling. When stratified by gender, the association was significant only among males (OR = 1.12, 95% CI 1.03, 1.22). The relationship between higher income inequality and greater odds for online gambling may be mediated by depressive and anxiety symptoms, psychosocial well-being, and school connectedness. Evidence points to further health consequences, such as online gambling participation, stemming from exposure to income inequality.
Keyphrases
- mental health
- physical activity
- health information
- social media
- sleep quality
- mental illness
- depressive symptoms
- body mass index
- metabolic syndrome
- public health
- young adults
- electronic health record
- bipolar disorder
- magnetic resonance imaging
- machine learning
- magnetic resonance
- deep learning
- computed tomography
- climate change
- artificial intelligence