Income and gambling disorder: A longitudinal matched case-control study with registry data from Norway

Abstract

Background Untangling the association between gambling disorder (GD) and income is complex. Financial strain is often a consequence of GD. At the same time GD is more prevalent in the context of poverty, suggesting income may be a risk marker for GD.

Aims The aim of the present study was to investigate whether income is a risk marker for GD and whether the longitudinal average predicted income for patients with GD between 2008 and 2018 compared to control groups. The study also explored the potential heterogeneity in income trajectories for patients with GD and associated characteristics.

Methods A matched case-control longitudinal study was conducted using two Norwegian registries (i.e., the Norwegian Patient Registry and the Division of Welfare Statistics). A total of 65,771 participants were included, 5131 who were diagnosed with GD (cases), 30,467 diagnosed with any other psychiatric or somatic disorder (control), and 30,164 from the general population (control). Multinomial and ordinary least squares regressions, along with group-based trajectory models were estimated.

Results Individuals with GD were more likely to have income levels in the bottom quartile of the nationally reported average income in 2008 compared to the general population. However, this was not observed in the psychiatric/somatic group. Both GD and psychiatric/somatic groups were less likely to have average/above average income compared to the general population. Expected income for patients with GD was below national averages between 2008 and 2018, with significant group differences identified. Estimated trajectories for patients with GD resulted in a seven-group model. Males were more likely to have membership in higher income groups, whereas females and younger GD patients were more likely to belong to trajectory groups with the lowest income.

Conclusion The results suggest income is a risk marker for GD. Heterogeneity present across the income distribution for patients with GD, coupled with identifiable patient characteristics, may help in prediction and screening of GD.