Exploring the mechanisms underlying increased risk of depressive disorder in ethnic minority populations in Europe: A causal loop diagram.

BACKGROUND: Multiple ethnic minority populations in Europe show high risk of major depressive disorder (MDD), with ethnic discrimination and low socioeconomic position (SEP) as established risk factors. How this risk is shaped by the interactions between these, and other social factors, remains to be elucidated. We aimed to develop a causal-loop diagram (CLD) to gain a better understanding of how factors at the intersection of ethnic discrimination and SEP dynamically interact to drive MDD risk. METHODS: We iteratively mapped the interactions and feedback loops between factors at the intersection of ethnic discrimination and SEP, drawing input from (i) a series of two interviews with a range of MDD domain experts, (ii) an existing CLD mapping the onset of MDD across psychological, biological, and social dimensions at the level of the individual, and (iii) other relevant literature. RESULTS: Through tracing the feedback loops in the resulting CLD, we identified ten driving mechanisms for MDD onset in ethnic minorities (two related to ethnic discrimination, SEP, social network and support, and acculturation, as well as one relating to the living environment and self-stigma towards MDD); and four factors that modulate these mechanisms (recent migration, religious affiliation, neighborhood social environment, and public stigma towards MDD). The intersecting nature of ethnic discrimination and SEP, combined with the reinforcing dynamics of the identified driving mechanisms across time- and spatial scales, underscores the excess exposure to circumstances that increase MDD risk in ethnic minorities. CONCLUSIONS: While this CLD requires validation through future studies, the intersecting and reinforcing nature of the identified driving mechanisms highlights that tackling the high risk of MDD in ethnic minorities may require intervening at multiple targets, from the individual (e.g., psychological interventions targeting negative beliefs or reducing stress) to the societal level (e.g., addressing labor market discrimination).

A Note on the Structural Change Test in Highly Parameterized Psychometric Models.

Equal parameter estimates across subgroups is a substantial requirement of statistical tests. Ignoring subgroup differences poses a threat to study replicability, model specification, and theory development. Structural change tests are a powerful statistical technique to assess parameter invariance. A core element of those tests is the empirical fluctuation process. In the case of parameter invariance, the fluctuation process asymptotically follows a Brownian bridge. This asymptotic assumption further provides the basis for inference. However, the empirical fluctuation process does not follow a Brownian bridge in small samples, and this situation is amplified in large psychometric models. Therefore, common methods of obtaining the sampling distribution are invalid and the structural change test becomes conservative. We discuss an alternative solution to obtaining the sampling distribution-permutation approaches. Permutation approaches estimate the sampling distribution through resampling of the dataset, avoiding distributional assumptions. Hereby, the tests power are improved. We conclude that the permutation alternative is superior to standard asymptotic approximations of the sampling distribution.

Modeling alcohol use disorder as a set of interconnected symptoms - Assessing differences between clinical and population samples and across external factors.

Alcohol use disorder is argued to be a highly complex disorder influenced by a multitude of factors on different levels. Common research approaches fail to capture this breadth of interconnecting symptoms. To address this gap in theoretical assumptions and methodological approaches, we used a network analysis to assess the interplay of alcohol use disorder symptoms. We applied the exploratory analysis to two US-datasets, a population sample with 23,591 individuals and a clinical sample with 483 individuals seeking treatment for alcohol use disorder. Using a Bayesian framework, we first investigated differences between the clinical and population sample looking at the symptom interactions and underlying structure space. In the population sample the time spent drinking alcohol was most strongly connected, whereas in the clinical sample loss of control showed most connections. Furthermore, the clinical sample demonstrated less connections, however, estimates were too unstable to conclude the sparsity of the network. Second, for the population sample we assessed whether the network was measurement invariant across external factors like age, gender, ethnicity and income. The network differed across all factors, especially for age subgroups, indicating that subgroup specific networks should be considered when deriving implications for theory building or intervention planning. Our findings corroborate known theories of alcohol use disorder stating loss of control as a central symptom in alcohol dependent individuals.