Analyzing service descriptors and patients’ clinical characteristics may help understand heterogeneity in long-term trajectory of patients with schizophrenia, bipolar and major depressive disorder

One major obstacle to advancing research and treatment for major psychiatric disorders is their substantial within-diagnosis heterogeneity in patient lifetime trajectories. Adapted research methods such as cluster analysis to define subgroups of patients are currently used. However few studies have included service delivery descriptors in cluster analysis to investigate the determinants of heterogeneity in long-term trajectories. The aim of this study was to test whether patterns of service delivery could help in defining subgroups in terms of trajectories and clinical profiles in schizophrenia, bipolar disorder or major depressive disorder patients. Hierarchical Agglomerative Clustering (HAC) algorithms were used on a sample extracted from a Quebec government (Canada) transactional database to group and classify patients according to their interactions with the service delivery system. The resulting clusters were analyzed using statistical tools to characterize service trajectories. We observed three distinct trajectories that were not specific to any one of the three lifetime psychiatric diagnoses. Clusters were particularly affected by varying rates of clinician changes across the trajectory and changes of diagnoses. Results suggest that incorporating service delivery characteristics in future longitudinal studies of heterogeneity might be useful as a complement to studies that solely examine patients’ clinical features. The inclusion of service delivery elements may also be a useful tool for acquiring knowledge to adapt services to patients’ needs in public mental health and mental health economics research.