This study addresses the much-discussed issue of the relationship between health and income. In particular, it focuses on the relation between mental health and household income by using generalized additive models of location, scale and shape and thus employing a distributional perspective. Furthermore, this study aims to give guidelines to applied researchers interested in taking a distributional perspective on health inequalities. In our analysis we use cross-sectional data of the German socioeconomic Panel (SOEP). We find that when not only looking at the expected mental health score of an individual but also at other distributional aspects, like the risk of moderate and severe mental illness, that the relationship between income and mental health is much more pronounced. We thus show that taking a distributional perspective, can add to and indeed enrich the mostly mean-based assessment of existent health inequalities.

Dropping out of psychotherapeutic treatment (i.e., the patient ending treatment unilaterally) poses a problem for patients, therapists, and the health care sector. Previous research showed that changes in symptom severity and general change mechanisms (GCMs), such as interpersonal experiences, intrapersonal experiences, and problem actuation, might be related to drop-out. We investigated the relationship of these predictors and drop-out in a sample of 724 patients (21.1% drop-out) receiving cognitivebehavioral therapy in routine care from a German outpatient clinic. Survival analysis was used to account for the longitudinal nature of the data created by routine outcome monitoring and to deal with the time varying predictors, GCMs, and changes in symptom severity. As outcome, we predicted the risk of dropping out. Results showed that patient-and therapist-rated interpersonal experiences, which include alliance, significantly predicted the risk for drop-out. Contrary to previous research, intrapersonal experiences and symptom severity change did not predict drop-out. Overall, GCMs and symptom severity change accounted for 3.8% of explained variance in the outcome. These results entail that it is important to monitor interpersonal experiences over the course of treatment to identify patients at risk for drop-out. Public Significance StatementRealization of general change mechanisms and changes in symptom severity over the course of treatment were used to predict drop-out in a survival analysis. Interpersonal experiences showed a significant association with drop-out and it is therefore advisable to monitor interpersonal experiences on a regular basis.

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