TY  - JOUR
AU  - Vonneilich, Nico
AU  - Becher, Heiko
AU  - Berger, Klaus
AU  - Bohmann, Patricia
AU  - Brenner, Hermann
AU  - Castell, Stefanie
AU  - Dragano, Nico
AU  - Harth, Volker
AU  - Jaskulski, Stefanie
AU  - Karch, André
AU  - Keil, Thomas
AU  - Krist, Lilian
AU  - Lange, Berit
AU  - Leitzmann, Michael
AU  - Massag, Janka
AU  - Meinke-Franze, Claudia
AU  - Mikolajczyk, Rafael
AU  - Obi, Nadia
AU  - Pischon, Tobias
AU  - Reuter, Marvin
AU  - Schmidt, Börge
AU  - Velásquez, Ilais Moreno
AU  - Völzke, Henry
AU  - Wiessner, Christian
AU  - von dem Knesebeck, Olaf
AU  - Lüdecke, Daniel
TI  - Depressive symptoms, education, gender and history of migration - an intersectional analysis using data from the German National Cohort (NAKO).
JO  - International journal for equity in health
VL  - 24
IS  - 1
SN  - 1475-9276
CY  - London
PB  - BioMed Central
M1  - DKFZ-2025-00846
SP  - 108
PY  - 2025
AB  - The educational gradient in depressive symptoms is well documented. Gender and history of migration have also been found to be associated with depressive symptoms. Intersectional approaches enable the analysis of the interplay of different social factors at a time to gain a deeper understanding of inequalities in depressive symptoms. In this study, intersectional inequalities in depressive symptoms according to education, gender and history of migration are analysed.The German National Cohort (NAKO, N = 204,783) collected information on depressive symptoms (PHQ-9), which was used as an outcome variable. Educational attainment (ISCED-97), gender, and history of migration constituted the different social strata in the analyses. The predicted probabilities of depressive symptoms for 30 social strata were calculated. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was applied, using logistic regression and social strata were introduced as higher-level unit interaction terms.The analyses revealed an educational gradient in depressive symptoms, with differences within each educational group when gender and history of migration were introduced to the models. The predicted probabilities of depressive symptoms varied between the most advantaged and the most disadvantaged social strata by more than 20
KW  - Humans
KW  - Germany: epidemiology
KW  - Male
KW  - Female
KW  - Educational Status
KW  - Depression: epidemiology
KW  - Adult
KW  - Middle Aged
KW  - Cohort Studies
KW  - Sex Factors
KW  - Socioeconomic Factors
KW  - Aged
KW  - Health Status Disparities
KW  - Depression (Other)
KW  - Educational inequalities (Other)
KW  - Gender (Other)
KW  - German national cohort (Other)
KW  - History of migration (Other)
KW  - Intersectional analysis (Other)
KW  - MAIHDA (Other)
KW  - NAKO (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40259268
DO  - DOI:10.1186/s12939-025-02479-2
UR  - https://inrepo02.dkfz.de/record/300632
ER  -