A Bayesian approach for exploring the effects of household income on self-reported mental health measures during the COVID-19 pandemic - Simon Busch-Moreno; Xiao Fu; Etienne B. Roesch (PLOS Mental Health, 2026)
The relationship between income and mental health is well-established yet complex, as both can be confounded by age, gender, and other demographic factors. To clarify these dynamics, this study examines the effects of age and income on symptoms of depression and generalised anxiety across female and male genders during the COVID-19 pandemic in the UK. Data from Wave-1 and Wave-6 of the COVID-19 Psychological Research Consortium (C19PRC) study were analysed using Bayesian hierarchical ordered-logistic mediation models. Results showed that older age was linked to less depression and anxiety in both genders. Higher income was linked to better mental health in males in both waves. For females, however, higher income was only weakly linked to more symptoms early in the pandemic, but by Wave‑6, it had become a substantially protective factor for their mental health. Additionally, males reported better mental health than females across most income groups, except at the lowest income group, where both genders experienced comparable distress. Using robust inferential statistical models, our findings explain the nuanced relationships between socioeconomic factors and mental health across genders during a public health crisis. This evidence can inform targeted mental health policies and economic support programs for future emergencies.