Multidimensional social inclusion and its prediction in schizophrenia spectrum disorder

Social inclusion is poor among patients with chronic disorders such as schizophrenia spectrum disorder (SSD). It significantly impacts patient life, healthcare and society. We aimed to study multidimensional social inclusion (mSI) among patients diagnosed with SSD, and to test the prediction of mSI. We used the baseline and 3-year follow-up data of 1,119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) cohort. The mSI was conceptualized by all subscales from social functioning (measured by Social Functioning Scale [SFS]) and quality of life (measured by the brief version of World Health Organization Quality of Life [WHOQOL-BREF]) questionnaires. K-means clustering was applied to identify mSI subgroups. Prediction models were built and internally validated via multinomial logistic regression (MLR) and random forest (RF) methods. Model fittings were compared by common factors, accuracy and the discriminability of mSI subgroups. We identified five mSI groups: “very low (social functioning)/very low (quality of life)”, “low/low”, “high/low”, “medium/high”, and “high/high”. The mSI was robustly predicted by genetic predisposition, premorbid social functioning, symptoms (i.e., positive, negative and depressive), number of met needs and baseline satisfaction with the environment and social life. The RF model was cautiously regarded to outperform the MLR model. We distinguished meaningful subgroups of mSI by combining rather than using two measurements standalone. The mSI subgroups were modestly predictable. The mSI has the potentials for personalized interventions to improve social recovery in patients. Different from conventional outcomes, we introduced mSI which has implications beyond clinics and could be applied to other disorders.