Socioeconomic Status as a Longitudinal Predictor of Negative Automatic Thoughts and Depression

Abstract

The aim of the present study is to examine socio-economic status (SES) as a longitudinal predictor of negative automatic thoughts (NAT) and depression. We collected data across three waves and tested a mediation model in which SES predicted NAT, which in turn predicted depression. SES was operationalized through multiple indicators, including education, material deprivation, income per  person (monthly), and perceived economic status. NAT was measured using the Automatic Thoughts Questionnaire (ATQ-15), and depression was assessed with the Beck Depression Inventory (BDI). A linear mixed model (LMM) was employed to account for
repeated measures and individual variability over time. Results indicated that NAT was a strong predictor of depression across waves, while some SES indicators (income per person and perceived economic status) were associated with NAT and depressive symptoms. Limitations included high attrition rates across waves and model warnings indicating potential instability in parameter estimates. These findings underscore the mediating role of cognitive patterns in the SES–depression relationship and highlight the need for early
cognitive interventions in socioeconomically disadvantaged populations.