Depressive symptomatology from a network perspective: Differences in the experience of symptoms involved in the self-recognition of depression and the diagnosis process by social position

Background: While social disparities in depression are well-documented, the symptom experience across social positions remains less studied. Aims: This study examines the connections between depressive symptoms and self-recognizing a depressive episode, on the one hand, and clinical diagnosis, on the other hand, by three social position indicators. Methods: We analyzed baseline data from a population-based cohort of adults living in France, grouping participants by three indicators: education, financial difficulties, and occupation, and stratifying by sex. Utilizing a psychometric network approach, we estimated 24 networks. Nodes corresponded to the 20 CES-D items and 1 external variable, either ‘Limitations due to depression’ or ‘Clinical depression’. Comparisons between socially disadvantaged and advantaged groups across the three social indicators were made in terms of network structures, global strength, and edge weights involving symptoms and both external nodes. Results: The study included data from 201,952 participants. Individuals in lower social positions exhibited higher rates of depressive-related variables. Four depressive symptoms emerged as crucial, being linked both to ‘Clinical depression’ and ‘Limitations’ across all social positions. Socially disadvantaged groups had denser networks. Some of the tests comparing network structures according to social position were significant, suggesting differences in the symptom activation chains. Connections between each external node and ‘Felt depressed’ and ‘Could not get going’ were non-invariant in educational and financial-based networks. Conclusions: Findings highlight four depressive symptoms, likely to play a key role in the experience of depression across all social positions. Other insights from specific symptoms could be used for improving depression care among disadvantaged populations.