Analysing risk factors for involuntary admission from an intersectional perspective: A latent class analysis

BACKGROUND: Involuntary admission (IA) is a contentious practice in mental healthcare, justified only to prevent harm to self or others. Previous studies have identified individual sociodemographic characteristics as risk factors for IA. Intersectional theories argue that such approaches overlook the complexities of social identities and related health inequalities. Intersectionality stresses the interconnected nature of social identities, such as race or class, and analyses how these overlapping factors create unique experiences of marginalisation and discrimination. This study aimed to adopt an intersectional framework to identify subgroups with specific sociodemographic characteristics and assess their risk for IA. We hypothesized that groups facing multiple forms of marginalisation would be strongly associated with higher risk for IA. METHODS: We analysed data from 16,024 cases at the Psychiatric University Clinic Zurich, Switzerland, between 2017 and 2020 using Latent Class Analysis to identify subgroups with distinct sociodemographic characteristics. Variables included sex, age, nationality, residence status, educational attainment, employment status, and language proficiency. Classes were validated against clinical factors including IA. RESULTS: Four distinct classes emerged. The class most strongly associated with IA was characterized by unemployment, social welfare dependency, non-European citizenship, temporary residency or refugee status, and low educational attainment. In contrast, classes with Swiss nationality, permanent residency, and employment were significantly less likely to experience IA. CONCLUSION: Adopting an intersectional framework, our findings suggest that individuals facing multiple marginalised identities are at higher risk for IA, indicating possible barriers to voluntary and early treatment. Further research is needed to explore and address these barriers.