Background: A proportion of people with mental health problems require longer term care in a psychiatric or social care institution. However, there are no internationally agreed quality standards for institutional care and no method to assess common care standards across countries.

BACKGROUND: Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations. METHODS: Positive and Negative Syndrome Scale data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients; 586 followed up after 1.35 6 0.70 years) were used for learning the factor structure by an orthonormal projective non-negative factorization. An international sample, pooled from 9 medical centers across Europe, the United States, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional magnetic resonance imaging connectivity patterns. RESULTS: A 4-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original Positive and Negative Syndrome Scale subscales and previously proposed factor models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventromedial frontal cortex, temporoparietal junction, and precuneus. CONCLUSIONS: Machine learning applied to multisite data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia.

BackgroundDespite the progress over recent decades in developing community mental health services internationally, many people still receive treatment and care in institutional settings. Those most likely to reside longest in these facilities have the most complex mental health problems and are at most risk of potential abuses of care and exploitation. This study aimed to develop an international, standardised toolkit to assess the quality of care in longer term hospital and community based mental health units, including the degree to which human rights, social inclusion and autonomy are promoted.MethodThe domains of care included in the toolkit were identified from a systematic literature review, international expert Delphi exercise, and review of care standards in ten European countries. The draft toolkit comprised 154 questions for unit managers. Inter-rater reliability was tested in 202 units across ten countries at different stages of deinstitutionalisation and development of community mental health services. Exploratory factor analysis was used to corroborate the allocation of items to domains. Feedback from those using the toolkit was collected about its usefulness and ease of completion.ResultsThe toolkit had excellent inter-rater reliability and few items with narrow spread of response. Unit managers found the content highly relevant and were able to complete it in around 90 minutes. Minimal refinement was required and the final version comprised 145 questions assessing seven domains of care.ConclusionsTriangulation of qualitative and quantitative evidence directed the development of a robust and comprehensive international quality assessment toolkit for units in highly variable socioeconomic and political contexts.