Impaired social functioning substantially affects the quality of life of patients with schizophrenia; however, it remains unclear whether the factors associated with social functioning differ according to relapse frequency. This study aimed to examine the differences in these factors among Japanese outpatients with schizophrenia, stratified by relapse frequency.

This nationwide cross-sectional study, the Multicenter Treatment Survey and Assessments for Schizophrenia in Psychiatric Clinics (MUSASI), was conducted in 330 psychiatric clinics in Japan between September and October 2023. A total of 10,081 patients diagnosed with schizophrenia-related disorders were analyzed. Patients were categorized as nonrelapsers, low-frequency relapsers (1–2 relapses), or high-frequency relapsers (≥3 relapses). Social functioning was assessed using the Social and Occupational Functioning Assessment Scale, with scores ≥61 defined as high functioning.

This study included 3,670 nonrelapsers, 4,428 low-frequency relapsers, and 1,983 high-frequency relapsers. Overall, 55.8% (n = 5,631) of patients were classified as having high social functioning. Across all groups, employment, shorter periods of instability during the past year, lower Clinical Global Impression–Severity scores, and fewer negative symptoms were significantly associated with higher social functioning. Group-specific associations were also observed: in nonrelapsers and low-frequency relapsers, positive symptoms and medication-related factors were relevant, whereas in low- and high-frequency relapsers, marital history was associated, and in high-frequency relapsers, the absence of tardive dyskinesia emerged as a factor.

Factors associated with social functioning differed according to relapse frequency, highlighting the need for relapse frequency-based, stratified intervention strategies.

Depression is increasingly recognized as a chronic and relapsing disorder. However, an important minority of patients who start treatment for their major depressive episode recover to euthymia. It is clinically important to be able to predict such individuals.

The study is a secondary analysis of a recently completed pragmatic megatrial examining first- and second-line treatments for hitherto untreated episodes of non-psychotic unipolar major depression (n = 2011). Using the first half of the cohort as the derivation set, we applied multiply-imputed stepwise logistic regression with backward selection to build a prediction model to predict remission, defined as scoring 4 or less on the Patient Health Quetionnaire-9 at week 9. We used three successively richer sets of predictors at baseline only, up to week 1, and up to week 3. We examined the external validity of the derived prediction models with the second half of the cohort.

In total, 37.0% (95% confidence interval 34.8–39.1%) were in remission at week 9. Only the models using data up to week 1 or 3 showed reasonable performance. Age, education, length of episode and depression severity remained in the multivariable prediction models. In the validation set, the discrimination of the prediction model was satisfactory with the area under the curve of 0.73 (0.70–0.77) and 0.82 (0.79–0.85), while the calibration was excellent with non-significant goodness-of-fit χ2 values (p = 0.41 and p = 0.29), respectively.

Patients and clinicians can use these prediction models to estimate their predicted probability of achieving remission after acute antidepressant therapy.